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Background Journey mapping involves the creation of visual narrative timelines depicting the multidimensional relationship between a consumer and a service. The use of journey maps in medical research is a novel and innovative approach to understanding patient healthcare encounters. Objectives To determine possible applications of journey mapping in medical research and the clinical setting. Specialist palliative care services were selected as the model to evaluate this paradigm, as there are numerous evidence gaps and inconsistencies in the delivery of care that may be addressed using this tool. Methods A purposive convenience sample of specialist palliative care providers from the Supportive and Palliative Care unit of a major Australian tertiary health service were invited to evaluate journey maps illustrating the final year of life of inpatient palliative care patients. Sixteen maps were purposively selected from a sample of 104 consecutive patients. This study utilised a qualitative mixed-methods approach, incorporating a modified Delphi technique and thematic analysis in an online questionnaire. Results Our thematic and Delphi analyses were congruent, with consensus findings consistent with emerging themes. Journey maps provided a holistic patient-centred perspective of care that characterised healthcare interactions within a longitudinal trajectory. Through these journey maps, participants were able to identify barriers to effective palliative care and opportunities to improve care delivery by observing patterns of patient function and healthcare encounters over multiple settings. Conclusions This unique qualitative study noted many promising applications of the journey mapping suitable for extrapolation outside of the palliative care setting as a review and audit tool, or a mechanism for providing proactive patient-centred care. This is particularly significant as machine learning and big data is increasingly applied to healthcare.

Multimodal data is rapidly growing in many fields of science and engineering, including single-cell biology. We introduce MultiMAP, a novel algorithm for dimensionality reduction and integration. MultiMAP can integrate any number of datasets, leverages features not present in all datasets, is not restricted to a linear mapping, allows the user to specify the influence of each dataset, and is extremely scalable to large datasets. We apply MultiMAP to single-cell transcriptomics, chromatin accessibility, methylation, and spatial data and show that it outperforms current approaches. On a new thymus dataset, we use MultiMAP to integrate cells along a temporal trajectory. This enables quantitative comparison of transcription factor expression and binding site accessibility over the course of T cell differentiation, revealing patterns of expression versus binding site opening kinetics.

Background Genotyping and sequencing technologies produce increasingly large numbers of genetic markers with potentially high rates of missing or erroneous data. Therefore, the construction of linkage maps is more and more complex. Moreover, the size of segregating populations remains constrained by cost issues and is less and less commensurate with the numbers of SNPs available. Thus, guaranteeing a statistically robust marker order requires that maps include only a carefully selected subset of SNPs. Results In this context, the SeSAM software allows automatic genetic map construction using seriation and placement approaches, to produce (1) a high-robustness framework map which includes as many markers as possible while keeping the order robustness beyond a given statistical threshold, and (2) a high-density total map including the framework plus almost all polymorphic markers. During this process, care is taken to limit the impact of genotyping errors and of missing data on mapping quality. SeSAM can be used with a wide range of biparental populations including from outcrossing species for which phases are inferred on-the-fly by maximum-likelihood during map elongation. The package also includes functions to simulate data sets, convert data formats, detect putative genotyping errors, visualize data and map quality (including graphical genotypes), and merge several maps into a consensus. SeSAM is also suitable for interactive map construction, by providing lower-level functions for 2-point and multipoint EM analyses. The software is implemented in a R package including functions in C++. Conclusions SeSAM is a fully automatic linkage mapping software designed to (1) produce a framework map as robust as desired by optimizing the selection of a subset of markers, and (2) produce a high-density map including almost all polymorphic markers. The software can be used with a wide range of biparental mapping populations including cases from outcrossing. SeSAM is freely available under a GNU GPL v3 license and works on Linux, Windows, and macOS platforms. It can be downloaded together with its user-manual and quick-start tutorial from ForgeMIA (SeSAM project) at https://forgemia.inra.fr/gqe-acep/sesam/-/releases

Background The Health Economics Research Centre (HERC) Database of Mapping Studies was established in 2013, based on a systematic review of studies developing mapping algorithms predicting EQ-5D. The Mapping onto Preference-based measures reporting Standards (MAPS) statement was published in 2015 to improve reporting of mapping studies. We aimed to update the systematic review and assess the extent to which recently-published studies mapping condition-specific quality of life or clinical measures to the EQ-5D follow the guidelines published in the MAPS Reporting Statement. Methods A published systematic review was updated using the original inclusion criteria to include studies published by December 2016. We included studies reporting novel algorithms mapping from any clinical measure or patient-reported quality of life measure to either the EQ-5D-3L or EQ-5D-5L. Titles and abstracts of all identified studies and the full text of papers published in 2016 were assessed against the MAPS checklist. Results The systematic review identified 144 mapping studies reporting 190 algorithms mapping from 110 different source instruments to EQ-5D. Of the 17 studies published in 2016, nine (53%) had titles that followed the MAPS statement guidance, although only two (12%) had abstracts that fully addressed all MAPS items. When the full text of these papers was assessed against the complete MAPS checklist, only two studies (12%) were found to fulfil or partly fulfil all criteria. Of the 141 papers (across all years) that included abstracts, the items on the MAPS statement checklist that were fulfilled by the largest number of studies comprised having a structured abstract (95%) and describing target instruments (91%) and source instruments (88%). Conclusions The number of published mapping studies continues to increase. Our updated database provides a convenient way to identify mapping studies for use in cost-utility analysis. Most recent studies do not fully address all items on the MAPS checklist.

Mixed methods research is essential to development of patient-reported outcome measures, digital technology, and endpoint selection for clinical drug trials and to advance clinical care when complex health-related experiences cannot be fully understood by quantitative or qualitative approaches alone. New technology and opportunities for remote data collection have changed the ways in which qualitative and quantitative data can be collected, enabling researchers to capture human experiences in ways not previously possible. This paper describes Perspective Mapping, a new online interviewing technique that uses mind mapping software to capture in-depth qualitative data inside a quantitative measurement framework to understand and measure individual experiences. The objective of this tutorial is to review the theoretical underpinnings, present instructions for study design and implementation, and address strengths, limitations, and potential applications of this technique in health and behavioral sciences. During videoconferencing interviews, mind-mapping software is used to visually depict experiences. Structured concept maps are cocreated in real time with participants, focusing on building detailed narrative descriptions about experiences and categorizing these within a predefined quantitative framework, such as the relative importance of different experiences relevant to a phenomenon. The approach combines semistructured interviewing with technology-enhanced card-sorting techniques, allowing participants to define and prioritize what matters most. This method ensures narrative richness alongside structured data collection, facilitating deeper understanding of phenomena. Perspective Mapping emphasizes participant engagement in data generation and analysis and enables the simultaneous collection of qualitative narratives and quantitative assessment of key concepts. The variations of the technique have been successfully applied in research on chronic illness, symptom burden, and digital health technology. Advantages of the approach include systematic collection of qualitative data, transparent and structured data outputs, real-time data validation, and the ability to return maps to participants as a form of reciprocity. Feasibility factors, such as interviewer capabilities, participant literacy, interview duration, and technology resources must be considered. Perspective Mapping offers an innovative and engaging way to gather complementary qualitative and quantitative data remotely. By blending qualitative depth with quantitative structure, the technique supports richer, more actionable insights for health research, policy, and beyond. This technique holds promise for applications in health, psychology, education, and other social sciences where comprehensive understanding of experiences is essential.

Mind mapping is a powerful technique that is often used for teaching declarative knowledge, but seldom implemented to record procedural knowledge. The present study focused on the latter. During a 12-week public presentation course, self-developed mind mapping software was utilized as a learning tool and an instrument to collect and analyze user behavior logs while summarizing and revising procedural knowledge. The participants were 53 working adults. They were divided into successful and unsuccessful mapping profiles based on their improvement. The pre- and post-tests on presentation skills, lag sequential analysis on log data, and interviews suggested that participants showing successful mapping behavior prioritized readability and ease of navigation of their maps. Their counterparts with unsuccessful mapping behavior tended to overload their maps and overuse highlighting. The discovery of actions and behavior patterns during the creation and revision of mind maps corresponding to successful/unsuccessful mind mapping profiles provides important suggestions to enhance existing digital mind mapping tools and to diagnose students who are falling behind. The implementation of mind mapping for procedural learning expands the area of mind mapping research and enlarges our understanding of teaching procedural knowledge.

Background and purpose Interpretation of T2 values remains difficult due to limited comparability across hardware and software systems and the lack of validated measurement recommendations for the number and orientation of mandatory slices. Our aims were to provide a standardized comparison of intra- and inter-individual T2 values in the short and long axes and to investigate inter-scanner reproducibility. Method and materials Ninety cardiovascular magnetic resonance (CMR) studies in 30 healthy subjects were performed with three identical 1.5 T CMR scanners (same hardware and software) using a balanced steady-state free precession (bSSFP) gradient echo sequence in three short axis (SAx) and three long axis (LAx) views. A commercially available T2 mapping software package of the latest generation with automatic in-line motion correction was used for acquisition. Regions of interest were manually drawn in each of the 16 myocardial segments according to the American Heart Association (AHA) model in three SAx and three LAx acquisitions. Analysis of inter-scanner, inter-segmental, intra-segmental, inter-regional and inter-level differences was performed. Results Inter-scanner reproducibility was high and the mean myocardial T2 value for all evaluated segments was 45.7 ± 3.4 ms. Significant inter-segmental variations of mean T2 values were found. Mean intra-segmental T2 values were comparable between LAx and SAx acquisitions in 72%. Significantly higher T2 values were found in apical segments and a significant disparity among different regions was found for SAx and LAx orientations. Conclusion Standardized cardiac T2 mapping is highly reproducible on identical CMR systems. T2 values vary significantly between single heart segments, regions, levels, and axes in young, healthy subjects.

Despite the numerous negative effects of tropical forest fires in Tanzania, the sources and effects remain insufficiently documented. This study aimed to develop an integrated approach that combines geospatial tools and socio-economic data to assess the sources and effects of forest fires and map burn severity and its trends over 10 years in West Usambara Mountain Forests. Three approaches including Participatory Rural Appraisal (PRA), satellite image analysis, and direct observation were used to generate information on spatial and temporal forest fire severity. Findings revealed that farm preparation (38.2%) and charcoal preparation (21.2%) are the primary source of these forest fires. Burn severity maps showed 32.12% to 20.31% of combined high and low severity areas, with a total burned area of 3,296.96 hectares, accounting for 15.86% of the reserves. The differenced Normalized Difference Vegetation Index (dNDVI) maps revealed 36.30% to 21.10 of high and low severity areas, while post-fire NBR and NDVI time series indicated a significant vegetation loss (0.21 to 0.36). This study demonstrates the integration of remote sensing and socio-economic approaches to enhance forest fire management, conservation, policy enforcement, and community awareness that can be upscaled to other forest areas for effective management.

Background Ticks are the primary vectors of numerous zoonotic pathogens, transmitting more pathogens than any other blood-feeding arthropod. In the northern hemisphere, tick-borne disease cases in humans, such as Lyme borreliosis and tick-borne encephalitis, have risen in recent years, and are a significant burden on public healthcare systems. The spread of these diseases is further reinforced by climate change, which leads to expanding tick habitats. Switzerland is among the countries in which tick-borne diseases are a major public health concern, with increasing incidence rates reported in recent years. Methods In response to these challenges, the “Tick Prevention” app was developed by the Zurich University of Applied Sciences and operated by A&K Strategy Ltd. in Switzerland. The app allows for the collection of large amounts of data on tick attachment to humans through a citizen science approach. In this study, citizen science data were utilized to map tick attachment to humans in Switzerland at a 100 m spatial resolution, on a monthly basis, for the years 2015 to 2021. The maps were created using a state-of-the-art modeling approach with the software extension spatialMaxent, which accounts for spatial autocorrelation when creating Maxent models. Results Our results consist of 84 maps displaying the risk of tick attachments to humans in Switzerland, with the model showing good overall performance, with median AUC ROC values ranging from 0.82 in 2018 to 0.92 in 2017 and 2021 and convincing spatial distribution, verified by tick experts for Switzerland. Our study reveals that tick attachment to humans is particularly high at the edges of settlement areas, especially in sparsely built-up suburban regions with green spaces, while it is lower in densely urbanized areas. Additionally, forested areas near cities also show increased risk levels. Conclusions This mapping aims to guide public health interventions to reduce human exposure to ticks and to inform the resource planning of healthcare facilities. Our findings suggest that citizen science data can be valuable for modeling and mapping tick attachment risk, indicating the potential of citizen science data for use in epidemiological surveillance and public healthcare planning. Graphical Abstract

Paraconvex multimaps are revisited in normed vector space setting. A parallel is provided with the studies conducted for real valued paraconvex functions on generalized convexities and monotonicities. Several characterizations are then obtained. The links with some generalized convexities for multimaps are examined and a first classification is achieved. In addition, two representation results for 2-paraconvex multimaps are given.