Text-based Tool Research Articles

MArVD2: a machine learning enhanced tool to discriminate between archaeal and bacterial viruses in viral datasets

Our knowledge of viral sequence space has exploded with advancing sequencing technologies and large-scale sampling and analytical efforts. Though archaea are important and abundant prokaryotes in many systems, our knowledge of archaeal viruses outside of extreme environments is limited. This largely stems from the lack of a robust, high-throughput, and systematic way to distinguish between bacterial and archaeal viruses in datasets of curated viruses. Here we upgrade our prior text-based tool (MArVD) via training and testing a random forest machine learning algorithm against a newly curated dataset of archaeal viruses. After optimization, MArVD2 presented a significant improvement over its predecessor in terms of scalability, usability, and flexibility, and will allow user-defined custom training datasets as archaeal virus discovery progresses. Benchmarking showed that a model trained with viral sequences from the hypersaline, marine, and hot spring environments correctly classified 85% of the archaeal viruses with a false detection rate below 2% using a random forest prediction threshold of 80% in a separate benchmarking dataset from the same habitats.

Acceptability and Feasibility of Text Message Interface to Assess Parents' Real-time PICU Experiences.

Parents of children in the pediatric intensive care unit (PICU) experience substantial stress; a parent's perception of their child's illness severity, more than objective measures, predicts psychological outcomes. No tools exist to assess parents' real-time experiences. This pilot study evaluated the feasibility and acceptability of a text-based tool to measure parental experience. Inclusion criteria included PICU stay >48 h, physician approval, smartphone access, and English-speaking caregiver. Eligible parents received a text-based baseline survey and surveys every other day while hospitalized regarding their mood/experiences and optional open-ended questions regarding stressors. They received post-discharge follow-up surveys at 1 week and 1, 3, and 6 months. Follow-up surveys assessed mood and symptoms of depression, anxiety, and post-traumatic stress. Interviews and surveys about the interface were conducted 1 week and 3 months following discharge. Feasibility was assessed by descriptive statistics (eg, response rates), and acceptability was assessed by descriptive statistics (survey results) and thematic analyses of interviews. Of 20 enrolled participants, the first 5 were excluded due to technical issues. Of the 15 included, results demonstrated feasibility and acceptability. Most participants (86%) completed all surveys during the PICU stay and continued to complete surveys at a high rate: 79%-94% 3 months post-discharge. All participants agreed that the system was easy to use and were satisfied with the system at discharge, and 91% remained satisfied 3 months post-discharge. Additionally, 76% reported comfort, and 69% reported benefit. From the interviews, participants lauded the system's convenience and applicability of content. Some proposed changes to improve ergonomics. Many suggested this interface could help teams better support families. A text-based interface for measuring experience in the PICU is feasible and acceptable to parents. Further research can explore how this could identify parents most at risk of adverse psychological sequelae and lead to earlier supportive interventions.

OneModel: an open-source SBML modeling tool focused on accessibility, simplicity and modularity

With the advent of the Systems Biology Markup Language (SBML), a large community of SBML-compliant tools has been created. However, these tools can only be used to their full potential by expert users with advanced programming knowledge. OneModel is an open-source text-based tool for defining SBML models in a modular and incremental way that minimizes the user's programming knowledge requirements. It is focused on accessibility, simplicity, and modularity. OneModel syntax allows the user to define models based on chemical (and pseudo-chemical) reactions, differential equations, and algebraic equations. OneModel is written in Python, and it provides two interfaces: a command-line interface for expert-users, and a graphical user interface for non-expert users. Here, we show two OneModel syntax use case scenarios for modeling an antithetic controller and then integrating it into a host-aware model, which is freely distributed with OneModel.

Iterative Text-Based Editing of Talking-Heads Using Neural Retargeting

We present a text-based tool for editing talking-head video that enables an iterative editing workflow. On each iteration users can edit the wording of the speech, further refine mouth motions if necessary to reduce artifacts, and manipulate non-verbal aspects of the performance by inserting mouth gestures (e.g., a smile) or changing the overall performance style (e.g., energetic, mumble). Our tool requires only 2 to 3 minutes of the target actor video and it synthesizes the video for each iteration in about 40 seconds, allowing users to quickly explore many editing possibilities as they iterate. Our approach is based on two key ideas. (1) We develop a fast phoneme search algorithm that can quickly identify phoneme-level subsequences of the source repository video that best match a desired edit. This enables our fast iteration loop. (2) We leverage a large repository of video of a source actor and develop a new self-supervised neural retargeting technique for transferring the mouth motions of the source actor to the target actor. This allows us to work with relatively short target actor videos, making our approach applicable in many real-world editing scenarios. Finally, our, refinement and performance controls give users the ability to further fine-tune the synthesized results.

Investigating and Promoting Trainee Science Teachers’ Conceptual Change of the Nature of Science with Digital Dialogue Games ‘InterLoc’

The purpose of this study is to explore how an online-structured dialogue environment supported (OSDE) collaborative learning about the nature of science among a group of trainee science teachers in the UK. The software used (InterLoc) is a linear text-based tool, designed to support structured argumentation with openers and ‘dialogue moves’. A design-based research approach was used to investigate multiple sessions using InterLoc with 65 trainee science teachers. Five participants who showed differential conceptual change in terms of their Nature of Science (NOS) views were purposively selected and closely followed throughout the study by using key event recall interviews. Initially, the majority of participants held naive views of NOS. Substantial and favourable changes in these views were evident as a result of the OSDE. An examination of the development of the five participants’ NOS views indicated that the effectiveness of the InterLoc discussions was mediated by cultural, cognitive, and experiential factors. The findings suggest that InterLoc can be effective in promoting reflection and conceptual change.

Practitioner research and formative assessment.

Several workplace-based formative assessment (WFA) tools exist; however, text-based feedback is minimal but valuable because residents have difficulty extracting meaning from numerical scores or rankings. Our programme lacked a formal WFA programme, so we aimed to develop and assess a primarily text-based tool, named formative assessment of skills in training (FAST), using action research. Action research (AR) methods, including iterative plan-act-observe-reflect cycles that included the FAST tool and our clinical context. Thirteen residents and 11 faculty members performed 133 assessments during three study cycles, and responded to post-use surveys. Overall, 83 per cent of participants indicated that FAST should be added to the resident curriculum. Time was perceived to be a barrier; however, time studies found that it did not prolong resident wait or patient visits. Changes were made to increase space for comments on the FAST form, and subsequently comment specificity and length increased. The FAST facilitates WFA in our programme and allows for specific written feedback with a copy retained by the resident. On average, it adds about 3 minutes to a clinic visit. The AR method facilitated WFA use, stakeholder buy-in, and FAST tool improvements.

Communicative NGOMSL: Development of an Evaluation Method for a Text-Based Communication System

Goals, Operators, Methods, and Selection rules (GOMS) is an analytic method and a model evaluation method for the knowledge necessary for a user to perform tasks in a system, and Natural GOMS Language (NGOMSL), a version of GOMS, is an attempt to define a language so that the GOMS model can be precisely constructed in a structured way. This study proposes Communicative NGOMSL, which is an extension of NGOMSL intended to accommodate modeling of text-based communication among two or more individuals. Besides the properties of GOMS techniques in evaluation, Communicative NGOMSL additionally considers common ground and communication time: Common ground refers to the mutual knowledge, beliefs, and assumptions of participants in communication, and communication time refers to the time that an individual waits for another to respond. The analysis of a Communicative NGOMSL model provides some information for a common task goal in a text-based communication system: (a) the relationship between the grounding process and Communicative NGOMSL statements, (b) the ratio of communication time to execution time for each individual, and (c) the balance among individuals in terms of learning time and mental workload. A demonstration of Communicative NGOMSL is provided through a sample case—a virtual text-based chat tool for exchanging addresses between two individuals. Future work needed to address the limitations of this study and further develop Communicative NGOMSL is discussed.

Use Case Diagrams in Support of Use Case Modeling

Use case modeling in the Unified Modeling Language (UML) is a popular text-based tool for systems analysis and design. Use cases can be used with or without supporting use case diagrams. This paper uses an experiment to explore the effectiveness of including a use case diagram with a set of use cases. The Cognitive Theory of Multimedia Learning is used to hypothesize that the use case diagram improves the effectiveness of use cases for novice users by providing visual cues aiding model viewers in selecting and integrating relevant information. The level of understanding developed by participants viewing either uses cases or use cases with a supporting use case diagram was measured using comprehension, retention, and problem solving tasks. Results showed that participants viewing the use cases with the supporting diagram developed a significantly higher level of understanding, as measured by performance on the problem solving task, than participants provided with use cases alone. This analysis suggests practitioners should consider combining a visual representation, such as a use case diagram, with text-based use cases to achieve higher levels of understanding in persons viewing these descriptions.

Cognitive Task Analysis of Electric Power System Control Center Operations

A cognitive task analysis of reliability coordinators in an electric power system control center was performed in conjunction with evaluation of three novel display solutions for line overload detection and correction. Eight experienced regional operations center operators participated in the study. The operators were shown two different geographic data view displays and a tabular, text-based tool, and interviewed as they solved three different transmission line overload problems using the different displays and tools. The operators were asked to think aloud as they performed the tasks. Their comments were recorded and transcribed, and used as a basis for the cognitive task analysis. The results indicated that although the operators liked the system data visualization approaches, they also placed high value on tabular presentation of text-based and numerical data. Visualization solutions should therefore be used to supplement existing displays and tools. This study also serves as an example of a participatory design approach to development of novel visualization solutions for complex task environments.

A powerful graphical pulse sequence programming tool for magnetic resonance imaging

A powerful graphical pulse sequence programming tool has been designed for creating magnetic resonance imaging (MRI) applications. It allows rapid development of pulse sequences in graphical mode (allowing for the visualization of sequences), and consists of three modules which include a graphical sequence editor, a parameter management module and a sequence compiler. Its key features are ease to use, flexibility and hardware independence. When graphic elements are combined with a certain text expressions, the graphical pulse sequence programming is as flexible as text-based programming tool. In addition, a hardware-independent design is implemented by using the strategy of two step compilations. To demonstrate the flexibility and the capability of this graphical sequence programming tool, a multi-slice fast spin echo experiment is performed on our home-made 0.3 T permanent magnet MRI system.