Clinical trainee performance on task-based AR/VR-guided surgical simulation is correlated with their 3D image spatial reasoning scores

This paper describes a methodology for the assessment of training simulator-based computer-assisted intervention skills on an AR/VR-guided procedure making use of CT axial slice views for a neurosurgical procedure: external ventricular drain (EVD) placement. The task requires that trainees scroll through a stack of axial slices and form a mental representation of the anatomical structures in order to subsequently target the ventricles to insert an EVD. The process of observing the 2D CT image slices in order to build a mental representation of the 3D anatomical structures is the skill being taught, along with the cognitive control of the subsequent targeting, by planned motor actions, of the EVD tip to the ventricular system to drain cerebrospinal fluid (CSF). Convergence is established towards the validity of this assessment methodology by examining two objective measures of spatial reasoning, along with one subjective expert ranking methodology, and comparing these to AR/VR guidance. These measures have two components: the speed and accuracy of the targeting, which are used to derive the performance metric. Results of these correlations are presented for a population of PGY1 residents attending the Canadian Neurosurgical "Rookie Bootcamp" in 2019.

The Pixel Anomaly Detection Tool: a user-friendly GUI for classifying detector frames using machine-learning approaches

Data collection at X-ray free electron lasers has particular experimental challenges, such as continuous sample delivery or the use of novel ultrafast high-dynamic-range gain-switching X-ray detectors. This can result in a multitude of data artefacts, which can be detrimental to accurately determining structure-factor amplitudes for serial crystallography or single-particle imaging experiments. Here, a new data-classification tool is reported that offers a variety of machine-learning algorithms to sort data trained either on manual data sorting by the user or by profile fitting the intensity distribution on the detector based on the experiment. This is integrated into an easy-to-use graphical user interface, specifically designed to support the detectors, file formats and software available at most X-ray free electron laser facilities. The highly modular design makes the tool easily expandable to comply with other X-ray sources and detectors, and the supervised learning approach enables even the novice user to sort data containing unwanted artefacts or perform routine data-analysis tasks such as hit finding during an experiment, without needing to write code.

FLEXR GUI: a graphical user interface for multi-conformer modeling of proteins

Proteins are well known 'shapeshifters' which change conformation to function. In crystallography, multiple conformational states are often present within the crystal and the resulting electron-density map. Yet, explicitly incorporating alternative states into models to disentangle multi-conformer ensembles is challenging. We previously reported the tool FLEXR, which, within a few minutes, automatically separates conformational signal from noise and builds the corresponding, often missing, structural features into a multi-conformer model. To make the method widely accessible for routine multi-conformer building as part of the computational toolkit for macromolecular crystallography, we present a graphical user interface (GUI) for FLEXR, designed as a plugin for Coot 1. The GUI implementation seamlessly connects FLEXR models with the existing suite of validation and modeling tools available in Coot. We envision that FLEXR will aid crystallographers by increasing access to a multi-conformer modeling method that will ultimately lead to a better representation of protein conformational heterogeneity in the Protein Data Bank. In turn, deeper insights into the protein conformational landscape may inform biology or provide new opportunities for ligand design. The code is open source and freely available on GitHub at https://github.com/TheFischerLab/FLEXR-GUI.

Prolonging the Response Movement Reduces Commission Errors in a High-Go, Low-No-Go Target Detection Task and Composite Metrics of Performance Miss This Effect

OBJECTIVE

Expand research on the Sustained Attention to Response Task (SART) to a more applied agricultural target detection/selection task and examine the utility of various performance metrics, including composite measures of speed and accuracy, in a High-Go/Low-No-Go stimuli task.

BACKGROUND

Modified SARTs have been utilized to investigate mechanisms, such as failures of response inhibition, occurring in friendly fire and collateral damage incidents. Researchers have demonstrated that composite measures of speed and accuracy are useful for Low Go/High No-Go stimuli tasks, but this has not been demonstrated for High-Go/Low-No-Go tasks, such as the SART.

METHOD

Participants performed a modified SART, where they selected ("sprayed") images of weeds (Go stimuli) that appeared on a computer screen, while withholding to rarer soybean plant images (No-Go stimuli).

RESULTS

Response time was a function of distance from a central starting point. Participants committed commission errors (sprayed the soybeans) at a significantly higher rate when the stimuli appeared under the cursor centered on the screen for each trial. Participant's omission errors (failure to spray a weed) increased significantly as a function of distance. The composite measures examined were primarily influenced by response time and omission errors limiting their utility when commission errors are of particular interest.

CONCLUSION

Participants are far more accurate in their decision making when required to execute a longer duration motor task in High-Go/Low-No-Go experiments.

APPLICATION

Demonstrates a serious human factors liability of target detection and snap-to-target systems.

Evidence for Shifting Cognitive Strategies when Icons Appear in Unexpected Locations

OBJECTIVE

The present study examines the cognitive effects of placing icons in unexpected spatial locations within websites.

BACKGROUND

Prior research has revealed evidence for cognitive conflict when web icons occur in unexpected locations (e.g., cart, top left), generally consistent with a dynamical systems models. Here, we compare the relative strength of evidence for both dual and dynamical systems models.

METHODS

Participants clicked on icons located in either expected (e.g., cart, top right) or unexpected (e.g., cart, top left) locations while mouse trajectories were continuously recorded. Trajectories were classified according to prototypes associated with each cognitive model. The dynamical systems model predicts curved trajectories, while the dual-systems model predicts straight and change of mind trajectories.

RESULTS

Trajectory classification revealed that curved trajectories increased (+11%), while straight and change of mind trajectories decreased (-12%) when target icons occurred in unexpected locations (p < .001).

CONCLUSION

Rather than employing a single cognitive strategy, users shift from a primarily dual-systems to dynamical systems strategy when icons occur in unexpected locations.

APPLICATION

Potential applications of this work include the assessment of cognitive impacts such as mental workload and cognitive conflict during real-time interaction with websites and other screen-based interfaces, personalization and adaptive interfaces based on an individual's cognitive strategy, and data-driven A/B testing of alternative interface designs.

XDSGUI: a graphical user interface for XDS, SHELX and ARCIMBOLDO

XDSGUI is a lightweight graphical user interface (GUI) for the XDS, SHELX and ARCIMBOLDO program packages that serves both novice and experienced users in obtaining optimal processing and phasing results for X-ray, neutron and electron diffraction data. The design of the program enables data processing and phasing without command line usage, and supports advanced command flows in a simple user-modifiable and user-extensible way. The GUI supplies graphical information based on the tabular log output of the programs, which is more intuitive, comprehensible and efficient than text output can be.

Conceptual Design and Prototyping for a Primate Health History Knowledge Model

Primate models are important for understanding human conditions, especially in studies of ageing, pathology, adaptation, and evolution. However, how to integrate data from multiple disciplines and render them compatible with each other for datamining and in-depth study is always challenging. In a long-term project, we have started a collaborative research endeavor to examine the health history of a free-ranging rhesus macaque colony at Cayo Santiago, and build a knowledge model for anthropological and biomedical/translational studies of the effects of environment and genetics on bone development, aging, and pathologies. This paper discusses the conceptual design as well as the prototyping of this model and related graphical user interfaces, and how these will help future scientific queries and studies.

DatView: a graphical user interface for visualizing and querying large data sets in serial femtosecond crystallography

DatView is a new graphical user interface (GUI) for plotting parameters to explore correlations, identify outliers and export subsets of data. It was designed to simplify and expedite analysis of very large unmerged serial femtosecond crystallography (SFX) data sets composed of indexing results from hundreds of thousands of microcrystal diffraction patterns. However, DatView works with any tabulated data, offering its functionality to many applications outside serial crystallography. In DatView's user-friendly GUI, selections are drawn onto plots and synchronized across all other plots, so correlations between multiple parameters in large multi-parameter data sets can be rapidly identified. It also includes an item viewer for displaying images in the current selection alongside the associated metadata. For serial crystallography data processed by indexamajig from CrystFEL [White, Kirian, Martin, Aquila, Nass, Barty & Chapman (2012 ▸). J. Appl. Cryst. 45, 335-341], DatView generates a table of parameters and metadata from stream files and, optionally, the associated HDF5 files. By combining the functionality of several commonly needed tools for SFX in a single GUI that operates on tabulated data, the time needed to load and calculate statistics from large data sets is reduced. This paper describes how DatView facilitates (i) efficient feedback during data collection by examining trends in time, sample position or any parameter, (ii) determination of optimal indexing and integration parameters via the comparison mode, (iii) identification of systematic errors in unmerged SFX data sets, and (iv) sorting and highly flexible data filtering (plot selections, Boolean filters and more), including direct export of subset CrystFEL stream files for further processing.

Non-invasive platform to estimate fasting blood glucose levels from salivary electrochemical parameters

Diabetes is a metabolic disorder that affects more than 400 million people worldwide. Most existing approaches for measuring fasting blood glucose levels (FBGLs) are invasive. This work presents a proof-of-concept study in which saliva is used as a proxy biofluid to estimate FBGL. Saliva collected from 175 volunteers was analysed using portable, handheld sensors to measure its electrochemical properties such as conductivity, redox potential, pH and K⁺, Na⁺ and Ca²⁺ ionic concentrations. These data, along with the person's gender and age, were trained and tested after casewise annotation with their true FBGL values using a set of mathematical algorithms. An accuracy of 87.4 ± 1.7% and a mean relative deviation of 14.1% (R² = 0.76) was achieved using a mathematical algorithm. All parameters except the gender were found to play a key role in the FBGL determination process. Finally, the individual electrochemical sensors were integrated into a single platform and interfaced with the authors’ algorithm through a simple graphical user interface. The system was revalidated on 60 new saliva samples and gave an accuracy of 81.67 ± 2.53% (R² = 0.71). This study paves the way for rapid, efficient and painless FBGL estimation from saliva.

Projections as visual aids for classification system design

Dimensionality reduction is a compelling alternative for high-dimensional data visualization. This method provides insight into high-dimensional feature spaces by mapping relationships between observations (high-dimensional vectors) to low (two or three) dimensional spaces. These low-dimensional representations support tasks such as outlier and group detection based on direct visualization. Supervised learning, a subfield of machine learning, is also concerned with observations. A key task in supervised learning consists of assigning class labels to observations based on generalization from previous experience. Effective development of such classification systems depends on many choices, including features descriptors, learning algorithms, and hyperparameters. These choices are not trivial, and there is no simple recipe to improve classification systems that perform poorly. In this context, we first propose the use of visual representations based on dimensionality reduction (projections) for predictive feedback on classification efficacy. Second, we propose a projection-based visual analytics methodology, and supportive tooling, that can be used to improve classification systems through feature selection. We evaluate our proposal through experiments involving four datasets and three representative learning algorithms.

xINTERPDF: a graphical user interface for analyzing intermolecular pair distribution functions of organic compounds from X-ray total scattering data

A new software program, xINTERPDF, that analyzes the intermolecular correlations in organic compounds via measured X-ray total scattering data is described.

A new software program, xINTERPDF, that analyzes the intermolecular correlations in organic compounds via measured X-ray total scattering data is described.

CCP4i2: the new graphical user interface to the CCP4 program suite

The CCP4 (Collaborative Computational Project, Number 4) software suite for macromolecular structure determination by X-ray crystallography groups brings together many programs and libraries that, by means of well established conventions, interoperate effectively without adhering to strict design guidelines. Because of this inherent flexibility, users are often presented with diverse, even divergent, choices for solving every type of problem. Recently, CCP4 introduced CCP4i2, a modern graphical interface designed to help structural biologists to navigate the process of structure determination, with an emphasis on pipelining and the streamlined presentation of results. In addition, CCP4i2 provides a framework for writing structure-solution scripts that can be built up incrementally to create increasingly automatic procedures.

Designing an Ontology for Emotion-driven Visual Representations

Emotions influence our perceptions and decisions and are often felt more strongly in situations related to healthcare. Therefore, it is important to understand how both providers and patients express their emotions in face-to-face scenarios. An ontology is a way to represent domain concepts and the relationships between them in a polyarchical manner. We have created an ontological model called the Visualized Emotion Ontology (VEO) that expresses the semantic definitions and visualizations of 25 emotions based on published research. With VEO, we can augment patient-facing software tools, like embodied conversational agents, to improve patient-provider interaction in clinical environments.

An editor for the generation and customization of geometry restraints

Obtaining a restraint dictionary for novel ligands or improving restraints for known ligands can require their manual modification. This can be tedious and error-prone. REEL is a restraints editor that provides quick, easy and accurate development, allowing global changes and fine-tuning of individual restraints.

Chemical restraints for use in macromolecular structure refinement are produced by a variety of methods, including a number of programs that use chemical information to generate the required bond, angle, dihedral, chiral and planar restraints. These programs help to automate the process and therefore minimize the errors that could otherwise occur if it were performed manually. Furthermore, restraint-dictionary generation programs can incorporate chemical and other prior knowledge to provide reasonable choices of types and values. However, the use of restraints to define the geometry of a molecule is an approximation introduced with efficiency in mind. The representation of a bond as a parabolic function is a convenience and does not reflect the true variability in even the simplest of molecules. Another complicating factor is the interplay of the molecule with other parts of the macromolecular model. Finally, difficult situations arise from molecules with rare or unusual moieties that may not have their conformational space fully explored. These factors give rise to the need for an interactive editor for WYSIWYG interactions with the restraints and molecule. Restraints Editor, Especially Ligands (REEL) is a graphical user interface for simple and error-free editing along with additional features to provide greater control of the restraint dictionaries in macromolecular refinement.

E-servant: an intelligent, programmable system to support and integrate assisted living technologies

The 'E-servant', a programmable system to control and manage assistive technologies, telehealth and telecare devices in a home environment is presented. The E-servant is programmed using a simple graphical interface that allows the user to build a dialogue in the form of a production rule system, which is triggered by a patient- or technology-initiated event. The patient interacts with the system through a personalised user interface to reach their goal of completing a task. These tasks, which the authors call 'scenarios', can be designed for users of different abilities (cognitive and/or physical). They can also be given priority levels, for example if a potential emergency situation arises in the patient's home, a scenario associated with the sensing of this event takes highest priority. The research presented in this Letter outlines the E-servant, its programming tool and reports its evaluation in living laboratory settings. The results suggest that it can be used as a central management system for supporting an integrated support environment for facilitating healthcare and activities of daily living, especially for older patients.