Free Copy Research Articles

Collaborative Forensic Autopsy Documentation and Supervised Report Generation Using a Hybrid Mixed-Reality Environment and Generative AI.

Forensic investigation is a complex procedure involving experts working together to establish cause of death and report findings to legal authorities. While new technologies are being developed to provide better post-mortem imaging capabilities-including mixed-reality (MR) tools to support 3D visualisation of such data-these tools do not integrate seamlessly into their existing collaborative workflow and report authoring process, requiring extra steps, e.g. to extract imagery from the MR tool and combine with physical autopsy findings for inclusion in the report. Therefore, in this work we design and evaluate a new forensic autopsy report generation workflow and present a novel documentation system using hybrid mixed-reality approaches to integrate visualisation, voice and hand interaction, as well as collaboration and procedure recording. Our preliminary findings indicate that this approach has the potential to improve data management, aid reviewability, and thus, achieve more robust standards. Further, it potentially streamlines report generation and minimise dependency on external tools and assistance, reducing autopsy time and related costs. This system also offers significant potential for education. A free copy of this paper and all supplemental materials are available at https://osf.io/ygfzx.

Investigating the Visual Utility of Differentially Private Scatterplots.

Increasingly, visualization practitioners are working with, using, and studying private and sensitive data. There can be many stakeholders interested in the resulting analyses-but widespread sharing of the data can cause harm to individuals, companies, and organizations. Practitioners are increasingly turning to differential privacy to enable public data sharing with a guaranteed amount of privacy. Differential privacy algorithms do this by aggregating data statistics with noise, and this now-private data can be released visually with differentially private scatterplots. While the private visual output is affected by the algorithm choice, privacy level, bin number, data distribution, and user task, there is little guidance on how to choose and balance the effect of these parameters. To address this gap, we had experts examine 1,200 differentially private scatterplots created with a variety of parameter choices and tested their ability to see aggregate patterns in the private output (i.e. the visual utility of the chart). We synthesized these results to provide easy-to-use guidance for visualization practitioners releasing private data through scatterplots. Our findings also provide a ground truth for visual utility, which we use to benchmark automated utility metrics from various fields. We demonstrate how multi-scale structural similarity (MS-SSIM), the metric most strongly correlated with our study's utility results, can be used to optimize parameter selection.

SPROUT: an Interactive Authoring Tool for Generating Programming Tutorials with the Visualization of Large Language Models.

The rapid development of large language models (LLMs), such as ChatGPT, has revolutionized the efficiency of creating programming tutorials. LLMs can be instructed with text prompts to generate comprehensive text descriptions for code snippets provided by users. However, the lack of transparency in the end-to-end generation process has hindered the understanding of model behavior and limited user control over the generated results. To tackle this challenge, we introduce a novel approach that breaks down the programming tutorial creation task into actionable steps. By employing the tree-of-thought method, LLMs engage in an exploratory process to generate diverse and faithful programming tutorials. We then present SPROUT, an authoring tool equipped with a series of interactive visualizations that empower users to have greater control and understanding of the programming tutorial creation process. A formal user study demonstrated the effectiveness of SPROUT, showing that our tool assists users to actively participate in the programming tutorial creation process, leading to more reliable and customizable results. By providing users with greater control and understanding, SPROUT enhances the user experience and improves the overall quality of programming tutorial.A free copy of this paper and all supplemental materials are available at https://osf.io/uez2t/?view only = 5102e958802341daa414707646428f86.

Get Free Copy: a multi-repository search platform for biomedical publications

We introduce Get Free Copy (https://getfreecopy.com), a web-based platform designed to streamline the search for biomedical literature across major repositories like arXiv, bioRxiv, medRxiv, and PubMed Central (PMC). Addressing challenges posed by paywalls and fragmented databases, it offers a unified interface for efficient retrieval of free, legitimate copies of biomedical literature. The platform’s implementation involves a Node.js backend and dynamic front-end display, enhancing accessibility and research efficiency. As an open-source project, Get Free Copy represents a significant contribution to the open-access movement, inviting global researcher collaboration for further development.

Evaluating Graph Layout Algorithms: A Systematic Review of Methods and Best Practices

AbstractEvaluations—encompassing computational evaluations, benchmarks and user studies—are essential tools for validating the performance and applicability of graph and network layout algorithms (also known as graph drawing). These evaluations not only offer significant insights into an algorithm's performance and capabilities, but also assist the reader in determining if the algorithm is suitable for a specific purpose, such as handling graphs with a high volume of nodes or dense graphs. Unfortunately, there is no standard approach for evaluating layout algorithms. Prior work holds a ‘Wild West’ of diverse benchmark datasets and data characteristics, as well as varied evaluation metrics and ways to report results. It is often difficult to compare layout algorithms without first implementing them and then running your own evaluation. In this systematic review, we delve into the myriad of methodologies employed to conduct evaluations—the utilized techniques, reported outcomes and the pros and cons of choosing one approach over another. Our examination extends beyond computational evaluations, encompassing user‐centric evaluations, thus presenting a comprehensive understanding of algorithm validation. This systematic review—and its accompanying website—guides readers through evaluation types, the types of results reported, and the available benchmark datasets and their data characteristics. Our objective is to provide a valuable resource for readers to understand and effectively apply various evaluation methods for graph layout algorithms. A free copy of this paper and all supplemental material is available at osf.io, and the categorized papers are accessible on our website at https://visdunneright.github.io/gd‐comp‐eval/.

The Military Research Library of Imperial Engineering Academy and School

The comprehensive studies on the history of military research libraries are scarce. The authors discuss the history of the library of Imperial Engineering Academy and School; analyze the development of library collection of this institution. The authors chronologize t he h istory o f t he A cademy a nd i ts l ibrary. The relevant regulatory and legal documents that defined the library operation and development, as well as the Academy’s and School’s annual reports, made the basis of the study. The key collection development sources are specified, namely purchase, funded subscriptions to periodicals, free copies of publications by the General Headquarters Military Typography, gifts, and exchange with other institutions. For the first time, the list of employees for the years 1869–1915 is compiled with the specification of their contribution to the library and teaching activities. The authors argue that the Academy’s book collection is the largest research sector library preserving the best military engineering and academic literature of 15th – early 20th centuries

A tight interplay between platelet activation and mitochondrial DNA release promotes platelet storage lesion in platelet concentrates.

Platelet storage lesion (PSL) adversely affects the quality of platelet concentrates (PCs). Platelets are prone to activation during storage. Moreover, elevated free mitochondrial DNA (mtDNA) levels in PCs are associated with a higher risk of adverse transfusion reactions. Therefore, we aimed to evaluate the correlation between platelet activation markers and mtDNA release during PC storage. Six PCs prepared by the platelet-rich plasma method were assessed for free mtDNA copy number using quantitative real-time PCR and CD62P (P-selectin) expression by flow cytometry on days 0 (PC collection day), 3, 5 and 7 of storage. Lactate dehydrogenase (LDH) activity, pH, platelet count, mean platelet volume (MPV) and platelet distribution width (PDW) were measured as well. The correlation between free mtDNA and other PSL parameters, and the correlation between all parameters, was determined. Significant increases in free mtDNA, MPV and PDW, and a significant decrease in platelet count and pH were observed. CD62P expression and LDH activity elevated significantly, particularly on storage days 5-7 and 0-3, respectively. Moreover, a moderate positive correlation (r = 0.61) was observed between free mtDNA and CD62P expression. The r values between free mtDNA and LDH, pH, platelet count, MPV and PDW were 0.81, -0.72, -0.49, 0.81 and 0.77, respectively. The interplay between platelet activation and mtDNA release in promoting PSL in PCs may serve as a promising target for future research on applying additive solutions and evaluating the quality of PCs to improve transfusion and clinical outcomes.

Quality Metrics and Reordering Strategies for Revealing Patterns in BioFabric Visualizations.

Visualizing relational data is crucial for understanding complex connections between entities in social networks, political affiliations, or biological interactions. Well-known representations like node-link diagrams and adjacency matrices offer valuable insights, but their effectiveness relies on the ability to identify patterns in the underlying topological structure. Reordering strategies and layout algorithms play a vital role in the visualization process since the arrangement of nodes, edges, or cells influences the visibility of these patterns. The BioFabric visualization combines elements of node-link diagrams and adjacency matrices, leveraging the strengths of both, the visual clarity of node-link diagrams and the tabular organization of adjacency matrices. A unique characteristic of BioFabric is the possibility to reorder nodes and edges separately. This raises the question of which combination of layout algorithms best reveals certain patterns. In this paper, we discuss patterns and anti-patterns in BioFabric, such as staircases or escalators, relate them to already established patterns, and propose metrics to evaluate their quality. Based on these quality metrics, we compared combinations of well-established reordering techniques applied to BioFabric with a well-known benchmark data set. Our experiments indicate that the edge order has a stronger influence on revealing patterns than the node layout. The results show that the best combination for revealing staircases is a barycentric node layout, together with an edge order based on node indices and length. Our research contributes a first building block for many promising future research directions, which we also share and discuss. A free copy of this paper and all supplemental materials are available at https://osf.io/9mt8r/?view_only = b70dfbe550e3404f83059afdc60184c6.

Illuminating the Landscape of Differential Privacy: An Interview Study on the Use of Visualization in Real-World Deployments.

As Differential Privacy (DP) transitions from theory to practice, visualization has surfaced as a catalyst in promoting acceptance and usage. Despite the potential of visualization tools to support differential privacy implementation, their development is limited by a lack of understanding of the overall deployment process, practitioner challenges, and the role of visual tools in real-world deployments. To narrow this gap, we interviewed 18 professionals from various backgrounds who regularly engage with differential privacy in their work. Our objectives were to understand the differential privacy implementation process and associated challenges; explore the actors (individuals involved in differential privacy implementation), how they use or struggle to use visualization; and identify the benefits and challenges of using visualization in the implementation process. Our results delineate the differential privacy implementation process into five distinct stages and highlight the main actors alongside the diverse visualization applications and shortcomings. We find that visualizations can be used to build foundational differential privacy knowledge, describe implementation parameters, and evaluate private outputs. However, the visualization strategies described often fail to address the diverse technical backgrounds and varied privacy and accuracy concerns of users, hindering effective communication between the different actors involved in the implementation process. From our findings, we propose three research directions: visualizations for setting and evaluating noise addition, evaluation of uncertainty visualization related to trust in differential privacy, and research focused on pedagogical visualizations for complex data science topics. A free copy of this paper and all supplemental materials are available at https://osf.io/qhyzt/?view_only=1a5c7d7553c840ab9f125d88bc13946f.

Evaluating and extending speedup techniques for optimal crossing minimization in layered graph drawings.

A layered graph is an important category of graph in which every node is assigned to a layer, and layers are drawn as parallel or radial lines. They are commonly used to display temporal data or hierarchical graphs. Previous research has demonstrated that minimizing edge crossings is the most important criterion to consider when looking to improve the readability of such graphs. While heuristic approaches exist for crossing minimization, we are interested in optimal approaches to the problem that prioritize human readability over computational scalability. We aim to improve the usefulness and applicability of such optimal methods by understanding and improving their scalability to larger graphs. This paper categorizes and evaluates the state-of-the-art linear programming formulations for exact crossing minimization and describes nine new and existing techniques that could plausibly accelerate the optimization algorithm. Through a computational evaluation, we explore each technique's effect on calculation time and how the techniques assist or inhibit one another, allowing researchers and practitioners to adapt them to the characteristics of their graphs. Our best-performing techniques yielded a median improvement of 2.5-17× depending on the solver used, giving us the capability to create optimal layouts faster and for larger graphs. We provide an open-source implementation of our methodology in Python, where users can pick which combination of techniques to enable according to their use case. A free copy of this paper and all supplemental materials, datasets used, and source code are available at https://osf.io/5vq79.

Ko Te Reo ō Ngā Tāngata / The People’s Voice

Ko Te Reo ō Ngā Tāngata / The People’s Voice

Cell Free MYC DNA Copy Number Identifies High-Risk Smouldering Myeloma and Newly Diagnosed Multiple Myeloma

Introduction Smouldering multiple myeloma (SMM) is a heterogenous condition, with some patients progressing rapidly (high-risk) while others have MGUS-like disease (low-risk). The International Myeloma Working Group's (IMWG) 2/20/20 risk score predicts 2-year progression, but despite these improvements in risk-stratification, identification of patients requiring early intervention remains elusive. For newly diagnosed multiple myeloma (NDMM), survival varies, and current risk-stratification falls short for poor responders to standard first-line therapy (high-risk). MYC is a potent oncogene implicated in the initiation, maintenance, and progression of various malignancies. MYC overexpression on immunohistochemistry predicts poor clinical outcome in MM 1, implicating MYC overexpression in aggressive disease kinetics in MM. We hypothesised that MYC copy number could improve identification of high-risk SMM and ND MM. In this study, we developed a minimally invasive liquid biopsy approach to characterise the expression of MYC copy number in circulating cell-free DNA (cfDNA) on a unique cohort of peripheral blood samples obtained from the national liquid biopsy biobank, the M1000 that is linked to our clinical registry, the Myeloma and Related Diseases Registry (MRDR). Methods We obtained plasma samples from 64 SMM and 149 ND MM patients from the M1000. cfDNA was extracted from 1mL of plasma collected in Streck™ tubes using QIAamp Circulating Nucleic Acid Kit (QIAGEN) according to manufacturer instructions. Equal amount of cfDNA (5 uL), run in duplicates, from each patient was utilised for copy number assessment developed using the Biorad QX200 Digital Droplet TM PCR. The copy number of MYC was calculated by comparing the number of molecules (using a ratio of positive to negative droplets) to the number of molecules arising from the reference (RPP30) genomic locus using the QuantaSoft TM Analysis Pro Software (Biorad). MYC copy number were correlated with clinical data from MRDR on progression in SMM and overall survival (OS) in NDMM. We use median copy number as cut off to categorise high and low MYC. Results We were able to extract cfDNA from 1 ml of plasma samples from both SMM and NDMM patients, with the median amount being 35.11 ng (11.9 ng - 107.78ng) and 50.26 ng (5.85 ng - 243.45 ng), respectively. The median MYC copy number levels were 1.883 in SMM and 1.860 for NDMM. Within 36 months follow up in the SMM cohort, 18 patients progressed to MM with median time to progression of 14 months (2- 36 months, Figure 1). High MYC copy number (>1.9) at diagnosis was predictive of disease progression with hazard ratio of 5.27 (95% CI 1.99 -14; P = 0.0015). When combined with the 20/20/20 risk stratification model 3, MYC copy number improved the reliability of this score. In the NDMM cohort, median OS for the cohort was not reached at 36 months follow up. Patients with high MYC copy number (>1.9) at diagnosis had shorter OS at 36 months compared to those with low MYC copy number at 36 months (66.05% vs 82.02%. P=0.0012, Figure 2). Conclusion Our study demonstrates a significant association between MYC copy number, assessed via plasma cfDNA, and the risk of progression in patients with SMM. Additionally, in NDMM, MYC copy number at diagnosis directly correlates with OS. These findings require further investigation and validation in a larger cohort of M1000 samples, as they have the potential to introduce a minimally-invasive risk stratification methodology to identify high-risk patients and the introduction of MYC targeted therapies in patients with high MYC copy number.

The Impact of Occlusion on Depth Perception at Arm's Length.

This paper investigates the accuracy of Augmented Reality (AR) technologies, particularly commercially available optical see-through displays, in depicting virtual content inside the human body for surgical planning. Their inherent limitations result in inaccuracies in perceived object positioning. We examine how occlusion, specifically with opaque surfaces, affects perceived depth of virtual objects at arm's length working distances. A custom apparatus with a half-silvered mirror was developed, providing accurate depth cues excluding occlusion, differing from commercial displays. We carried out a study, contrasting our apparatus with a HoloLens 2, involving a depth estimation task under varied surface complexities and illuminations. In addition, we explored the effects of creating a virtual "hole" in the surface. Subjects' depth estimation accuracy and confidence were assessed. Results showed more depth estimation variation with HoloLens and significant depth error beneath complex occluding surfaces. However, creating a virtual hole significantly reduced depth errors and increased subjects' confidence, irrespective of accuracy enhancement. These findings have important implications for the design and use of mixed-reality technologies in surgical applications, and industrial applications such as using virtual content to guide maintenance or repair of components hidden beneath the opaque outer surface of equipment. A free copy of this paper and all supplemental materials are available at https://bit.ly/3YbkwjU.

Quantivine: A Visualization Approach for Large-Scale Quantum Circuit Representation and Analysis.

Quantum computing is a rapidly evolving field that enables exponential speed-up over classical algorithms. At the heart of this revolutionary technology are quantum circuits, which serve as vital tools for implementing, analyzing, and optimizing quantum algorithms. Recent advancements in quantum computing and the increasing capability of quantum devices have led to the development of more complex quantum circuits. However, traditional quantum circuit diagrams suffer from scalability and readability issues, which limit the efficiency of analysis and optimization processes. In this research, we propose a novel visualization approach for large-scale quantum circuits by adopting semantic analysis to facilitate the comprehension of quantum circuits. We first exploit meta-data and semantic information extracted from the underlying code of quantum circuits to create component segmentations and pattern abstractions, allowing for easier wrangling of massive circuit diagrams. We then develop Quantivine, an interactive system for exploring and understanding quantum circuits. A series of novel circuit visualizations is designed to uncover contextual details such as qubit provenance, parallelism, and entanglement. The effectiveness of Quantivine is demonstrated through two usage scenarios of quantum circuits with up to 100 qubits and a formal user evaluation with quantum experts. A free copy of this paper and all supplemental materials are available at https://osf.io/2m9yh/?view_only=0aa1618c97244f5093cd7ce15f1431f9.

Enthusiastic and Grounded, Avoidant and Cautious: Understanding Public Receptivity to Data and Visualizations.

Despite an abundance of open data initiatives aimed to inform and empower "general" audiences, we still know little about the ways people outside of traditional data analysis communities experience and engage with public data and visualizations. To investigate this gap, we present results from an in-depth qualitative interview study with 19 participants from diverse ethnic, occupational, and demographic backgrounds. Our findings characterize a set of lived experiences with open data and visualizations in the domain of energy consumption, production, and transmission. This work exposes information receptivity - an individual's transient state of willingness or openness to receive information -as a blind spot for the data visualization community, complementary to but distinct from previous notions of data visualization literacy and engagement. We observed four clusters of receptivity responses to data- and visualization-based rhetoric: Information-Avoidant, Data-Cautious, Data-Enthusiastic, and Domain-Grounded. Based on our findings, we highlight research opportunities for the visualization community. This exploratory work identifies the existence of diverse receptivity responses, highlighting the need to consider audiences with varying levels of openness to new information. Our findings also suggest new approaches for improving the accessibility and inclusivity of open data and visualization initiatives targeted at broad audiences. A free copy of this paper and all supplemental materials are available at https://OSF.IO/MPQ32.

Marjorie: Visualizing Type 1 Diabetes Data to Support Pattern Exploration.

In this work we propose Marjorie, a visual analytics approach to address the challenge of analyzing patients' diabetes data during brief regular appointments with their diabetologists. Designed in consultation with diabetologists, Marjorie uses a combination of visual and algorithmic methods to support the exploration of patterns in the data. Patterns of interest include seasonal variations of the glucose profiles, and non-periodic patterns such as fluctuations around mealtimes or periods of hypoglycemia (i.e., glucose levels below the normal range). We introduce a unique representation of glucose data based on modified horizon graphs and hierarchical clustering of adjacent carbohydrate or insulin entries. Semantic zooming allows the exploration of patterns on different levels of temporal detail. We evaluated our solution in a case study, which demonstrated Marjorie's potential to provide valuable insights into therapy parameters and unfavorable eating habits, among others. The study results and informal feedback collected from target users suggest that Marjorie effectively supports patients and diabetologists in the joint exploration of patterns in diabetes data, potentially enabling more informed treatment decisions. A free copy of this paper and all supplemental materials are available at https://osf.io/34t8c/.

Ggdist: Visualizations of Distributions and Uncertainty in the Grammar of Graphics.

The grammar of graphics is ubiquitous, providing the foundation for a variety of popular visualization tools and toolkits. Yet support for uncertainty visualization in the grammar graphics-beyond simple variations of error bars, uncertainty bands, and density plots-remains rudimentary. Research in uncertainty visualization has developed a rich variety of improved uncertainty visualizations, most of which are difficult to create in existing grammar of graphics implementations. ggdist, an extension to the popular ggplot2 grammar of graphics toolkit, is an attempt to rectify this situation. ggdist unifies a variety of uncertainty visualization types through the lens of distributional visualization, allowing functions of distributions to be mapped to directly to visual channels (aesthetics), making it straightforward to express a variety of (sometimes weird!) uncertainty visualization types. This distributional lens also offers a way to unify Bayesian and frequentist uncertainty visualization by formalizing the latter with the help of confidence distributions. In this paper, I offer a description of this uncertainty visualization paradigm and lessons learned from its development and adoption: ggdist has existed in some form for about six years (originally as part of the tidybayes R package for post-processing Bayesian models), and it has evolved substantially over that time, with several rewrites and API re-organizations as it changed in response to user feedback and expanded to cover increasing varieties of uncertainty visualization types. Ultimately, given the huge expressive power of the grammar of graphics and the popularity of tools built on it, I hope a catalog of my experience with ggdist will provide a catalyst for further improvements to formalizations and implementations of uncertainty visualization in grammar of graphics ecosystems. A free copy of this paper is available at https://osf.io/2gsz6. All supplemental materials are available at https://github.com/mjskay/ggdist-paper and are archived on Zenodo at doi:10.5281/zenodo.7770984.

Welcome from the Editors

Welcome from the Editors

A prospective randomized controlled study on the curative effects of enteral immunonutrition support therapy in adult burn patients at nutritional risk

A prospective randomized controlled study on the curative effects of enteral immunonutrition support therapy in adult burn patients at nutritional risk

“There's really almost nobody there who's trying to make sense of it”

“There's really almost nobody there who's trying to make sense of it”