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Eye movements have a spatial (where people look), but also a temporal (when people look) component. Various types of visualizations have been proposed that take this spatio-temporal nature of the data into account, but it is unclear how well each one can be interpreted and whether such interpretation depends on the question asked about the data or the nature of the dataset that is being visualised. In this study, four spatio-temporal visualization techniques for eye movements (chord diagram, scan path, scarf plot, space-time cube) were compared in a user study. Participants (N = 25) answered three questions (what region first, what region most, which regions most between) about each visualization, which was based on two types of datasets (eye movements towards adverts, eye movements towards pairs of gambles). Accuracy of the answers depended on a combination of the dataset, the question that needed to answered, and the type of visualization. For most questions, the scan path, which did not use area of interest (AOI) information, resulted in lower accuracy than the other graphs. This suggests that AOIs improve the information conveyed by graphs. No effects of experience with reading graphs (for work or not for work) or education on accuracy of the answer was found. The results therefore suggest that there is no single best visualisation of the spatio-temporal aspects of eye movements. When visualising eye movement data, a user study may therefore be beneficial to determine the optimal visualization of the dataset and research question at hand. [ABSTRACT FROM AUTHOR]

Multiple time-series graphs are commonly used for data visualisation, but few scholars have investigated the impact of graphical attributes on decision-making efficiency. This study explores the effects of graphical attributes of varying redundancy conditions on decision-making efficiency. Two experimental conditions were developed for the experiment: non-redundant (independent graphical attributes: colour, linear and marker) and redundant (combinations of two and more graphical attributes: colour and linear, colour and marker, etc.). A total of 60 people took part in both experiments and performed two tasks: maximisation and discrimination. The experiments revealed that the addition of attributes, such as colour, marker or linear, decreased response time (RT), but the combination of colour & linear & marker increased RT. This is more significant in discrimination tasks. We provide empirical evidence for the design of time-series data visualisations and encourage the combination of two of these graphical attributes, such as colour & linear, colour & marker or linear & marker, when conditions allow, to improve decision-making efficiency. Practitioner Summary: Few scholars have studied the impact of graphical attributes on decision-making efficiency in data visualisation. This study explores the effect of graphical attributes with different redundancy levels on decision-making efficiency through behavioural experiments. It has been found that moderately redundant graphical attributes in difficult tasks can significantly improve decision-making efficiency. [ABSTRACT FROM AUTHOR]

Background Aims Methods Results Conclusions Optimal heart failure (HF) pharmacotherapy (guideline‐directed medical therapy and diuretics) in older people with frailty is uncertain due to limited evidence.To evaluate utilisation of HF pharmacotherapy and prevalence of polypharmacy, adverse drug events (ADEs), falls, delirium, renal impairment and duration of hospitalisation in older inpatients, according to frailty.A retrospective cross‐sectional study of the TO HOME cohort of 2000 inpatients ≥75 years admitted for ≥48 h to rehabilitation, geriatric or general medicine from 1 July 2016 to 30 June 2017 across six hospitals in Sydney, Australia. Data were collected from electronic medical records. International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Australian Modification identified HF diagnosis, ADEs and frailty using hospital frailty risk score. Outcomes included utilisation of HF pharmacotherapy; polypharmacy; ADEs, falls, delirium, renal and impairment; and duration of hospitalisation.Among 439 (22.0% of TO HOME cohort) patients with undifferentiated HF, 284 (69.5%) had intermediate or high risk of frailty, and 412 (94%) took ≥1 HF pharmacotherapy, with 357 (81.3%) patients on loop diuretics. Patients with high frailty risk frequently continued beta‐blockers (70%) and discontinued renin‐angiotensin system inhibitors (57%). Most patients experienced polypharmacy (n = 426, 97.0%). Renal impairment prevalence was 67%–76% across frailty groups. Increasing frailty risk (low, intermediate and high) was associated with increasing prevalence of ADEs (31%, 56% and 84%), falls (12%, 25% and 46%) and delirium (8%, 27% and 49%) and longer hospitalisation.Frailty, HF‐pharmacotherapy changes in hospital and ADEs were common among older inpatients with HF. The association of adverse outcomes according to frailty needs further investigation. Poor documentation of HF phenotype may be a barrier to medication optimisation in older inpatients. [ABSTRACT FROM AUTHOR]

Summary: We discuss two novel approaches to inter‐distributional comparisons in the classical two‐sample problem. Our starting point is properly standardised and combined, very popular in several areas of statistics and data analysis, ordinal dominance and receiver characteristic curves, denoted by ODC and ROC, respectively. The proposed new curves are termed the comparison curves. Their estimates, being weighted rank processes on (0,1), form the basis of inference. These weighted processes are intuitive, well‐suited for visual inspection of data at hand and are also useful for constructing some formal inferential procedures. They can be applied to several variants of two‐sample problem. Their use can help improve some existing procedures both in terms of power and the ability to identify the sources of departures from the postulated model. To simplify interpretation of finite sample results, we restrict attention to values of the processes on a finite grid of points. This results in the so‐called bar plots (B‐plots), which readably summarise the information contained in the data. What is more, we show that B‐plots along with adjusted simultaneous acceptance regions provide principled information about where the model departs from the data. This leads to a framework that facilitates identification of regions with locally significant differences. We show an implementation of the considered techniques to a standard stochastic dominance testing problem. Some min‐type statistics are introduced and investigated. A simulation study compares two tests pertinent to the comparison curves to well‐established tests in the literature and demonstrates the strong and competitive performance of the former in many typical situations. Some real data applications illustrate simplicity and practical usefulness of the proposed approaches. A range of other applications of considered weighted processes is briefly discussed too. [ABSTRACT FROM AUTHOR]

The paper is devoted to analysing the dynamics of visiting tourist destinations using geographic information systems. The paper explores the possibilities of modern geographic information systems for visualisation and analysis of spatial data related to tourist flows in order to further optimise the management of tourist facilities and infrastructure planning. The methodological basis of the study is based on the conceptual framework of spatial analysis and the theory of sustainable tourism development, which allows for the integration of different data. The data on the attendance of tourist sites collected on the basis of mobile operators’ data using geographic information systems are used as an information base. The analysis of the obtained data and visualisation are performed using modern programming languages. The main results include identifying key trends in tourist behaviour, assessing the impact of infrastructural factors on visitation and developing recommendations for improving tourism services. The findings of the study confirm the effectiveness of using geographic information systems to solve planning and management tasks in the tourism industry, which contributes to improving the attractiveness and competitiveness of tourist destinations.

BACKGROUND: Medical students are often taught clinical reasoning implicitly, rather than through a formal curriculum. Like qualified health professionals, they engage in a wide range of information seeking and other practices as part of the clinical reasoning process. This increasingly includes seeking out information online and being informed by anecdotal information from social media or peer groups. The aim of this research was to investigate how anecdotes and icon arrays influenced the clinical reasoning process of medical students deciding to prescribe a hypothetical new drug. METHODS: A cross-sectional survey design was used. The survey required participants to respond to six hypothetical clinical scenarios in which they were asked to prescribe a hypothetical drug "polypill" for a specific patient. The order of delivery of the six scenarios was randomised for each participant. In response to each scenario, participants indicated how effective they perceived each drug to be. The study received ethics approval from the University of Sydney Human Research Ethics Committee: Protocol No: 2019/001. All participants provided written informed consent before agreeing to participate in the study. RESULTS: A total of 56 medical students fully completed the survey. Statistical analysis of the responses indicated that the icon array may be effective for highlighting how the polypill reduces CVD risk, reducing the impact of anecdotes on efficacy judgments. Without the icon array, both the positive and negative anecdotes made participants less willing to prescribe the polypill. CONCLUSIONS: Medical student clinical reasoning processes appear to be influenced by anecdotal information and data visualisations. The extent of this influence is unclear, but there may be a need to actively educate students about the influence of these factors on their decision-making as they graduate into a world where they will be increasingly interacting with anecdotal information on social media and visualisations of electronic data. [ABSTRACT FROM AUTHOR]

What might methodological approaches drawing on Geographical Information Systems (GIS) and Social Network Analysis (SNA) offer to sub‐disciplines in geography which have traditionally been dominated by qualitative and often micro‐scale research, such as historical or political geography? How might these approaches—often understood as opposing—be brought together to advance transnational research in particular? This article responds to these questions through a reflection on a recent project on the geopolitics of diplomatic training in the mid‐twentieth century. Building on the established use of biography to focus transnational analyses within a complex abundance of sources, the project complemented such close‐reading with computational methods of distant‐reading, able to analyse large datasets to produce prosopographies and network visualisations that help identify diffuse and larger scale political and geographical relationships. The article concludes with a consideration of how such methods might be effectively integrated in the historical or political geographer's toolkit. [ABSTRACT FROM AUTHOR]

Rural America is often depicted as a distressed and left‐behind place, with limited opportunities for the children growing up there. This paper addresses this topic by examining the dynamics of rural places over the past four decades and how these changes impact the economic mobility of children raised in poor rural households. Employing a place‐based framework, we utilise sequence analysis to identify dominant trajectories of change for more than 8000 rural communities. Our analysis reveals highly diverse community trajectories that connect deindustrialisation and racial inequality to elevated and rising poverty rates in certain places, while also documenting more favourable poverty trends elsewhere. These diverging local outcomes shed new light on the conflicting narratives surrounding rural America. We then demonstrate that, among children from poorer households, exposure to community poverty is predictive of adult economic mobility, patterns which are partly mediated by family stability and child poverty. Our finding that poor children face additional disadvantages when they also grow up in poor places suggests a potential role for place‐based policies and redistribution to help ameliorate these disparities. [ABSTRACT FROM AUTHOR]

In the search for a repository site for high-level radioactive waste in Germany, the perception of safety and trust in the actors are central to public acceptance. In communicating safety, methods of safety assessment and the role of uncertainties need to be addressed. Given the complexity of the issue, there is a need for indicators that are suitable both for assessing the long-term safety of repositories and for communicating with the general public. Similarly, there is a requirement to communicate uncertainties in an accessible manner. The TRANSENS project provides basic research in nuclear waste management (NWM) and utilizes a transdisciplinary approach: Non-experts who are not directly affected by the site selection process and who have no stated interest in NWM are involved in the research process, as are practice actors. A series of four transdisciplinary workshops was specifically designed to explore the perspectives of individuals with a high level of disciplinary knowledge but no system knowledge of NWM. Participants were selected from doctoral students in science and technology who had no prior knowledge in this area. Two of these workshops address the questions stated above and are presented here. The article describes the considerations underlying the workshop planning and implementation phases, and the content developed in the workshops on indicator selection and visualisation of uncertainties. The participants compiled a list of desirable indicator properties, which showed a high degree of congruence with the relevant literature. A proposal for a database to collect, administer and assess uncertainties shows similarities with the approach followed by the German implementer and complements it with an interactive visualisation. Transdisciplinary work is resource-intensive and its use in a research context must be carefully considered for each individual application. A transdisciplinary approach was successfully used for the purposes of method validation, method optimisation and the development of disciplinary impulses. An application of transdisciplinary approaches for optimising the Safety Case of nuclear repositories is feasible. [ABSTRACT FROM AUTHOR]

Geographical research is increasingly focused on how digital technology shapes human-nature relations. This article explores how internet search engines and their associated algorithms and indexing technologies order and produce homogenising accounts of forest places. We put forward 'un-indexing' as a critical and inventive method for un-ordering and re-ordering search engine results to complicate digital perspectives on forest-society relations. We present Everything at the Forest Park, a series of four speculative catalogues we created to invite collective inquiries into the digital mediation of a forested area in Scotland – Queen Elizabeth Forest Park. Fostering a slower form of engagement with web material, the catalogues suggest how geographers and other scholars might critically repurpose, reappropriate and interrogate the algorithmically curated and advertising-oriented orderings of search engines to foster more careful and convivial forest-society relations. [ABSTRACT FROM AUTHOR]

The analysis and visualisation of antimicrobial resistance (AMR) surveillance data is a crucial challenge, especially in high-burden, low-middle-income countries. We describe the design, development, integration, and implementation of the Quick Analysis of Antimicrobial Patterns and Trends (QAAPT) tool for AMR data analysis and visualisation. The QAAPT tool was created by the Capturing Data on Antimicrobial Resistance Patterns and Trends in Use in Regions of Asia project, led by the International Vaccine Institute (IVI). This open-source web-based tool/application generates statistical and visual outputs of AMR data, offers data curation options, and can be integrated with laboratory information management systems. The QAAPT tool is user-friendly and is operable by someone with limited expertise in software or programming. As a part of the project, the tool was used to analyse data from 72 laboratories across 7 Asian countries. In this study, we present the technical aspects of tool development and highlight implementation outcomes for analysing and generating visual reports from more than 2.37 million highly heterogeneous antimicrobial susceptibility test data points.

Complexity theory provides an essential framework for analysing buildings as complex systems, influenced by the interaction of architectural, structural and building services elements. To manage this complexity, BIM methods and tools for Facility Management leverage the language of drawing to represent the complexity of data and optimise its management. The analysis of more than three thousand rooms of the Piedmont Region Palace as a case study for this contribution demonstrates how algorithms and automatisms can facilitate the management of large amounts of data to improve the visualisation and interpretation of information. This paper examines the value of graphical interfaces in revealing hidden logics of complexity and transforming them into resources available to users for a new decision support system. Article info Received: 10/09/2024; Revised: 11/10/2024; Accepted: 14/10/2024

Abstract Automatic detection of tomato leaf spot disease is essential for control and loss reduction. Traditional algorithms face challenges such as large amount of data, multiple training and heavy computation. In this study, a lightweight shared Siamese neural network method was proposed for tomato leaf disease identification, which is suitable for resource‐limited environments. Experiments on Plant‐Village, Taiwan and Taiwan ++ datasets show that the accuracy fluctuates very little even when trained with only 60% of the data, which confirms the effectiveness of the proposed method in the small data environment. In addition, compared with the mainstream algorithms, it improves the accuracy by up to 35.3%on Plant‐Village and two Taiwan datasets respectively. The experimental results also show that the proposed method still performs well when the data is imbalanced and the sample size is small.

Background: Data literacy (DL) is vital for teachers, as it enables them to build on data and improve teaching and learning. Therefore, developing DL among pre‐service teachers is critical. Objectives: The purpose of this study is threefold: to evaluate whether a feedback visualisation of peer assessment‐based teaching approach (FVPA‐based teaching approach) can (1) promote pre‐service teachers' DL; (2) enhance their learning motivation; and (3) improve their cognitive load. Methods: The research was conducted based on a pre‐test‐post‐test control group quasi‐experimental design. With 20 participants in the experimental group and 21 in the control group, a total of 41 pre‐service teachers were included in the study. The pre‐service teachers in the experimental group adopted the FVPA‐based teaching approach, and those in the control group adopted the traditional peer assessment‐based learning approach. Results and Conclusions: The experimental group participants outperformed the control group participants in DL, learning motivation, and cognitive load. FVPA was conducive to helping pre‐service teachers critically interpret data, understand their teaching and learning issues, and improve self‐reflection. The findings indicate a reciprocal relationship between learning motivation and DL; improving the learning motivation of pre‐service teachers could promote their DL. Implications: This study contributes to current knowledge by providing empirical evidence on the benefits of an FVPA‐based teaching approach in improving pre‐service teachers' DL, motivation, and cognitive load. The study findings, limitations, and prospects for future research are discussed. Lay Description: What is already known about this topic?: Teacher data literacy has emerged as a crucial competency for the forthcoming generation of educators, gaining attention from education institutions globally.Peer assessment, a well‐established practice in pre‐service teacher education, has proven effective in enhancing the learning experience for disadvantaged students by fostering motivation and improving academic performance.Despite the acknowledged benefits of feedback visualisation in facilitating self‐reflection, motivation, and academic performance, creating meaningful visualisations remains a challenge for educators and learners. What this paper adds?: This study introduces feedback visualisation in peer assessment to enhance comprehension of feedback information among pre‐service teachers.Feedback visualisation augments the benefits derived from peer assessment, refining data literacy, improving learning motivation, and alleviating cognitive load among pre‐service educators.The analysis of data directly relevant to pre‐service teachers and the provision of timely feedback can motivate critical analysis and utilisation of data, facilitating the identification of pedagogical and learning challenges.The study explores intricate interactions among learning motivation, data literacy, and cognitive load within the context of peer assessment visual feedback. The implications of study findings for practitioners: We encourage teacher educators to implement peer assessment instruction grounded in visual feedback for a comprehensive understanding of learning outcomes, identification of pedagogical challenges, determination of causes, and evidence‐informed instructional decision‐making.Teacher educator should diversify strategies to augment the motivation of pre‐service teachers, cultivating their proactive engagement in data analysis, interpretation, and application to enhance overall data literacy.We recommend that teacher educators provide rich visual feedback or present complex data in a visually accessible format for pre‐service teachers who need additional assistance to mitigate cognitive load and facilitate successful learning outcomes. [ABSTRACT FROM AUTHOR]

Preparation of DNA polymorphism datasets for analysis is an important step in evolutionary genetic and molecular ecology studies. Ever‐growing dataset sizes make this step time consuming, but few convenient software tools are available to facilitate processing of large‐scale datasets including thousands of sequence alignments. Here I report "processor of sequences v4" (proSeq4)—a user‐friendly multiplatform software for preparation and evolutionary genetic analyses of genome‐ or transcriptome‐scale sequence polymorphism datasets. The program has an easy‐to‐use graphic user interface and is designed to process and analyse many thousands of datasets. It supports over two dozen file formats, includes a flexible sequence editor and various tools for data visualization, quality control and most commonly used evolutionary genetic analyses, such as NJ‐phylogeny reconstruction, DNA polymorphism analyses and coalescent simulations. Command line tools (e.g. vcf2fasta) are also provided for easier integration into bioinformatic pipelines. Apart of molecular ecology and evolution research, proSeq4 may be useful for teaching, e.g. for visual illustration of different shapes of phylogenies generated with coalescent simulations in different scenarios. ProSeq4 source code and binaries for Windows, MacOS and Ubuntu are available from https://sourceforge.net/projects/proseq/. [ABSTRACT FROM AUTHOR]

Advances in cancer treatment have improved patient outcomes and survival in recent decades. Increased complexity, duration, and individualisation of treatment protocols present an important challenge for care teams monitoring adherence to best-practice care. A rigid rules-based system for flagging outliers is not fit for purpose, as there are sound reasons for deviating from baseline protocols, such as the management of treatment side effects to a tolerable degree, however the methods for determining the bounds of appropriateness for variation are not well studied or understood. The development of digital representations to inform cancer care delivery in a timely and continuing manner is crucial. This scoping review seeks to identify gaps in current methods and propose a novel approach to digitally represent patient journeys in clinically meaningful visual and computational forms. These methods can be combined to produce real-time, clinically applicable tools such as group-level business-intelligence dashboards (are processes and resources adequate to ensure that patients are being treated according to best practice?) as well as individual-level decision support (what is the likely outcome for this patient if treatment is stopped early based on prior data?) and day to day clinical workflows (what has happened to this patient so far?). [ABSTRACT FROM AUTHOR]

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Automatic detection of tomato leaf spot disease is essential for control and loss reduction. Traditional algorithms face challenges such as large amount of data, multiple training and heavy computation. In this study, a lightweight shared Siamese neural network method was proposed for tomato leaf disease identification, which is suitable for resource‐limited environments. Experiments on Plant‐Village, Taiwan and Taiwan ++ datasets show that the accuracy fluctuates very little even when trained with only 60% of the data, which confirms the effectiveness of the proposed method in the small data environment. In addition, compared with the mainstream algorithms, it improves the accuracy by up to 35.3%on Plant‐Village and two Taiwan datasets respectively. The experimental results also show that the proposed method still performs well when the data is imbalanced and the sample size is small. [ABSTRACT FROM AUTHOR]

The Upper Hunter Valley is a major coal mining area in New South Wales (NSW), Australia. Due to the ongoing increase in mining activities, PM10 (air-borne particles with an aerodynamic diameter less than 10 micrometres) pollution has become a major air quality concern in local communities. The present study was initiated to quantitatively examine the spatial and temporal variability of PM10 pollution in the region. An earlier paper of this study identified two air quality subregions in the valley. This paper aims to provide a holistic summarisation of the relationships between elevated PM10 pollution in two subregions and the local- and synoptic-scale meteorological conditions for spring and summer, when PM10 pollution is relatively high. A catalogue of twelve synoptic types and a set of six local meteorological patterns were quantitatively derived and linked to each other using the self-organising map (SOM) technique. The complex meteorology–air pollution relationships were visualised and interpreted on the SOM planes for two representative locations. It was found that the influence of local meteorological patterns differed significantly for mean PM10 levels vs. the occurrence of elevated pollution events and between air quality subregions. In contrast, synoptic types showed generally similar relationships with mean vs. elevated PM10 pollution in the valley. Two local meteorological patterns, the hot–dry–northwesterly wind conditions and the hot–dry–calm conditions, were found to be the most PM10 pollution conducive in the valley when combined with a set of synoptic counterparts. These synoptic types are featured with the influence of an eastward migrating continental high-pressure system and westerly troughs, or a ridge extending northwest towards coastal northern NSW or southern Queensland from the Tasman Sea. The method and results can be used in air quality research for other locations of NSW, or similar regions elsewhere. [ABSTRACT FROM AUTHOR]

Using a pre-post-test design, this study evaluated the impact of implementing a standard on the reliability of player management decision-making within a professional rugby union environment. Five practitioners from a High-Performance Unit (HPU) rated 22 instances of Global Positioning System (GPS)–based external training load information of 14 players across the 2021–2022 season. This rating was whether a peak/trough/normal exposure in load had occurred. The ratings were repeated at four time points (separated by 2 weeks) before (Pre1, Pre2) and after (Post1, Post2) implementing a consensus statement as a subjective standard (using a dashboard) developed previously within the same environment to identify peaks/troughs in player external training loads. Inter-rater agreement between individuals at each voting round was assessed using Light's Kappa, while pre-post-standard intra-rater agreement was determined from Cohen's Kappa (both with 95% confidence intervals). Changes to dashboard usability from implementing the standard were assessed by administering the System Usability Scale to 11 HPU staff at the four time points. Pre-standard moderate inter-rater agreement (Pre1: 0.53 (0.36–0.69), Pre2: 0.60 (0.42–0.77)) increased to almost perfect agreement (Post1: 0.74 (0.57–0.89), Post2: 0.90 (0.79–1)) post-standard. The intra-rater agreement of 2/5 participants was almost perfect post-standard, while it remained within substantial levels for the others. A linear mixed model (χ 2(3) = 8.85, p = 0.03) illustrated a slight increase in dashboard usability after incorporating the standard (Pre1: 84.09, Pre2: 81.36; Post1: 87.73, Post2: 87.27). Overall, the results highlighted that the subjective standard enhanced reliability of practitioner agreement for the selected decision. [ABSTRACT FROM AUTHOR]

Cytoscape is an open-source bioinformatics environment for the analysis, integration, visualization, and query of biological networks. In this perspective piece, we describe our project to bring the Cytoscape desktop application to the web while explaining our strategy in ways relevant to others in the bioinformatics community. We examine opportunities and challenges in developing bioinformatics software that spans both the desktop and web, and we describe our ongoing efforts to build a Cytoscape web application, highlighting the principles that guide our development.

Abstract In the field of scientific visualization, the upscaling of time‐varying volume is meaningful. It can be used in in situ visualization to help scientists overcome the limitations of I/O speed and storage capacity when analysing and visualizing large‐scale, time‐varying simulation data. This paper proposes self‐attention residual network‐based spatial super‐resolution (SARN‐SSR), a spatial super‐resolution model based on self‐attention residual networks that can generate time‐varying data with temporal coherence. SARN‐SSR consists of two components: a generator and a discriminator. The generator takes the low‐resolution volume sequences as the input and gives the corresponding high‐resolution volume sequences as the output. The discriminator takes both synthesized and real high‐resolution volume sequence as the input and gives a matrix to predict the realness as the output. To verify the validity of SARN‐SSR, four sets of time‐varying volume datasets are applied from scientific simulation. In addition, SARN‐SSR is compared on these datasets, both qualitatively and quantitatively, with two deep learning‐based techniques and one traditional technique. The experimental results show that by using this method, the closest time‐varying data to the ground truth can be obtained.

Abstract Kidney stones require surgical removal when they grow too large to be broken up externally or to pass on their own. Upper tract urothelial carcinoma is also sometimes treated endoscopically in a similar procedure. These surgeries are difficult, particularly for trainees who often miss tumours, stones or stone fragments, requiring re‐operation. Furthermore, there are no patient‐specific simulators to facilitate training or standardized visualization tools for ureteroscopy despite its high prevalence. Here a system ASSIST‐U is proposed to create realistic ureteroscopy images and videos solely using preoperative computerized tomography (CT) images to address these unmet needs. A 3D UNet model is trained to automatically segment CT images and construct 3D surfaces. These surfaces are then skeletonized for rendering. Finally, a style transfer model is trained using contrastive unpaired translation (CUT) to synthesize realistic ureteroscopy images. Cross validation on the CT segmentation model achieved a Dice score of 0.853 ± 0.084. CUT style transfer produced visually plausible images; the kernel inception distance to real ureteroscopy images was reduced from 0.198 (rendered) to 0.089 (synthesized). The entire pipeline from CT to synthesized ureteroscopy is also qualitatively demonstrated. The proposed ASSIST‐U system shows promise for aiding surgeons in the visualization of kidney ureteroscopy.

Non-linear dimensionality reduction can be performed by manifold learning approaches, such as stochastic neighbour embedding (SNE), locally linear embedding (LLE) and isometric feature mapping (ISOMAP). These methods aim to produce two or three latent embeddings, primarily to visualise the data in intelligible representations. This manuscript proposes extensions of Student's t-distributed SNE (t-SNE), LLE and ISOMAP, for dimensionality reduction and visualisation of multi-view data. Multi-view data refers to multiple types of data generated from the same samples. The proposed multi-view approaches provide more comprehensible projections of the samples compared to the ones obtained by visualising each data-view separately. Commonly, visualisation is used for identifying underlying patterns within the samples. By incorporating the obtained low-dimensional embeddings from the multi-view manifold approaches into the K-means clustering algorithm, it is shown that clusters of the samples are accurately identified. Through extensive comparisons of novel and existing multi-view manifold learning algorithms on real and synthetic data, the proposed multi-view extension of t-SNE, named multi-SNE, is found to have the best performance, quantified both qualitatively and quantitatively by assessing the clusterings obtained. The applicability of multi-SNE is illustrated by its implementation in the newly developed and challenging multi-omics single-cell data. The aim is to visualise and identify cell heterogeneity and cell types in biological tissues relevant to health and disease. In this application, multi-SNE provides an improved performance over single-view manifold learning approaches and a promising solution for unified clustering of multi-omics single-cell data. [ABSTRACT FROM AUTHOR]

In the field of scientific visualization, the upscaling of time‐varying volume is meaningful. It can be used in in situ visualization to help scientists overcome the limitations of I/O speed and storage capacity when analysing and visualizing large‐scale, time‐varying simulation data. This paper proposes self‐attention residual network‐based spatial super‐resolution (SARN‐SSR), a spatial super‐resolution model based on self‐attention residual networks that can generate time‐varying data with temporal coherence. SARN‐SSR consists of two components: a generator and a discriminator. The generator takes the low‐resolution volume sequences as the input and gives the corresponding high‐resolution volume sequences as the output. The discriminator takes both synthesized and real high‐resolution volume sequence as the input and gives a matrix to predict the realness as the output. To verify the validity of SARN‐SSR, four sets of time‐varying volume datasets are applied from scientific simulation. In addition, SARN‐SSR is compared on these datasets, both qualitatively and quantitatively, with two deep learning‐based techniques and one traditional technique. The experimental results show that by using this method, the closest time‐varying data to the ground truth can be obtained. [ABSTRACT FROM AUTHOR]

Graphs in research articles can increase the comprehension of statistical data but may mislead readers if poorly designed. We propose a new plot type, the sea stack plot, which combines vertical histograms and summary statistics to represent large univariate datasets accurately, usefully, and efficiently. We compare five commonly used plot types (dot and whisker plots, boxplots, density plots, univariate scatter plots, and dot plots) to assess their relative strengths and weaknesses when representing distributions of data commonly observed in biological studies. We find the assessed plot types are either difficult to read at large sample sizes or have the potential to misrepresent certain distributions of data, showing the need for an improved method of data visualisation. We present an analysis of the plot types used in four ecology and conservation journals covering multiple areas of these research fields, finding widespread use of uninformative bar charts and dot and whisker plots (60% of all panels showing univariate data from multiple groups for the purpose of comparison). Some articles presented more informative figures by combining plot types (16% of panels), generally boxplots and a second layer such as a flat density plot, to better display the data. This shows an appetite for more effective plot types within conservation and ecology, which may further increase if accurate and user‐friendly plot types were made available. Finally, we describe sea stack plots and explain how they overcome the weaknesses associated with other alternatives to uninformative plots when used for large and/or unevenly distributed data. We provide a tool to create sea stack plots with our R package 'seastackplot', available through GitHub. [ABSTRACT FROM AUTHOR]