Visual Analyses Research Articles

Testing the efficacy of a single-session intervention to reduce work-related distress

ABSTRACT To address the high levels of work-related stress experienced by public defender trial attorneys, a brief, single-session FACT intervention was implemented. A multiple-baseline, single-case design was employed to evaluate changes in general distress and psychological flexibility. A variety of visual, regression, parametric and non-parametric analyses were used. Results demonstrated significant reductions in distress for some participants, and increases in psychological flexibility for most. These findings suggest that brief ACT-based interventions may help psychotherapy clients increase their psychological flexibility when experiencing work-related distress.

Interpreting CNN models for musical instrument recognition using multi-spectrogram heatmap analysis: a preliminary study

IntroductionMusical instrument recognition is a critical component of music information retrieval (MIR), aimed at identifying and classifying instruments from audio recordings. This task poses significant challenges due to the complexity and variability of musical signals.MethodsIn this study, we employed convolutional neural networks (CNNs) to analyze the contributions of various spectrogram representations—STFT, Log-Mel, MFCC, Chroma, Spectral Contrast, and Tonnetz—to the classification of ten different musical instruments. The NSynth database was used for training and evaluation. Visual heatmap analysis and statistical metrics, including Difference Mean, KL Divergence, JS Divergence, and Earth Mover’s Distance, were utilized to assess feature importance and model interpretability.ResultsOur findings highlight the strengths and limitations of each spectrogram type in capturing distinctive features of different instruments. MFCC and Log-Mel spectrograms demonstrated superior performance across most instruments, while others provided insights into specific characteristics.DiscussionThis analysis provides some insights into optimizing spectrogram-based approaches for musical instrument recognition, offering guidance for future model development and improving interpretability through statistical and visual analyses.

The application of ChatGPT in nursing: a bibliometric and visualized analysis

ObjectiveWith the development of ChatGPT, the number of studies within the nursing field has increased. The sophisticated language capabilities of ChatGPT, coupled with its exceptional precision, offer significant support within the nursing field, which includes clinical nursing, nursing education, and the clinical decision-making process. Preliminary findings suggest positive outcomes, underscoring its potential as a valuable resource for enhancing clinical care. However, a comprehensive analysis of this domain is lacking, and the application of bibliometric methods remains rare. This study aims to describe and predict the developmental trajectory of the discipline, identify research hotspots and trends, and provide a comprehensive framework for the integration of ChatGPT in nursing.MethodsFollowing the development of a search strategy in collaboration with librarians, the implementation of this strategy occurred in the Web of Science Core Collection (WoSCC) on June 30, 2024. For bibliometric and visual analyses—including evaluations of sources, institutions, countries, author collaboration networks, and keywords—Bibliometrix (version 4.4.2) and CiteSpace (version 6.2.R2 Basic) were employed.ResultsA total of 81 articles published by 67 authors were retrieved from the Web of Science Core Collection database, covering the period of June 30, 2024. The number of published studies has exhibited an increasing trend. The “European Journal of Cardiovascular Nursing” emerged as the most productive journals, while the USA, the UK, and China were identified as the leading countries in terms of publication output. The top 10 keywords identified in this study include artificial intelligence, nursing education, large language models, ChatGPT, natural language processing, generative artificial intelligence, care, nursing practice, clinical decision-making, and deep learning.ConclusionChatGPT is an emerging tool in the nursing field, currently in the foundational research phase. While there is significant international collaboration, cooperation among author groups remains somewhat limited. Studies focusing on ChatGPT in nursing primarily concentrate on two key themes: (1) the deep learning of ChatGPT in nursing and (2) the feasibility of its application. It is essential for nurses across various specialties to collaborate in exploring the diverse applications of ChatGPT within their domains, thereby fostering the ongoing development and enhancement of this technology.

CNVizard—a lightweight streamlit application for an interactive analysis of copy number variants

BackgroundMethods to call, analyze and visualize copy number variations (CNVs) from massive parallel sequencing data have been widely adopted in clinical practice and genetic research. To enable a streamlined analysis of CNV data, comprehensive annotations and good visualizations are indispensable. The ability to detect single exon CNVs is another important feature for genetic testing. Nonetheless, most available open-source tools come with limitations in at least one of these areas. One additional drawback is that available tools deliver data in an unstructured and static format which requires subsequent visualization and formatting efforts.ResultsHere we present CNVizard, an interactive Streamlit app allowing a comprehensive visualization of CNVkit data. Furthermore, combining CNVizard with the CNVand pipeline allows the annotation and visualization of CNV or SV VCF files from any CNV caller.ConclusionCNVizard, in combination with CNVand, enables the comprehensive and streamlined analysis of short- and long-read sequencing data and provide an intuitive webapp-like experience enabling an interactive visualization of CNV data.

Intelligent visual analysis of accident behavior and mechanism inherent in ship collision accident data

The analysis of ship collision accident data plays a crucial role in ensuring maritime transportation safety. This paper proposes an intelligent visual analysis-based method focusing on the co-occurrence patterns, collision risk, similarity analysis, and classification of ship collision data. Based on data from 1573 maritime accidents that occurred globally between 2010 and 2023, this paper first designs co-occurrence patterns to identify regularities between accident attributes. Second, a fusion model combining fuzzy comprehensive evaluation and logistic regression is developed to calculate ship collision risk, and the model is applied to reconstruct and visually analyze the collision process. Third, the similarity between two collision accidents is analyzed based on Sentence-BERT algorithm, and a heterogeneous graph neural network model is constructed for accident classification. Finally, all the analysis contents are integrated into the visual analysis system of the ship collision accident data to illustrate the accident behavior and mechanism inherent in ship collision accident data. The rich content within the visualization system provides trainee crew members with a more comprehensive understanding of accidents and serves as a reference for maritime management agencies to analyze similar and related accidents, derive lessons learned, identify high-risk areas, and formulate targeted risk management strategies.

The influence of computer technologies used on the performance indicators and the risk of professional burnout of therapeutic doctors

The article tells that, to date, the impact of excessive use of computer technology (CT) on the performance of internists and the occurrence of professional burnout syndrome (PBS) has not been sufficiently studied. The authors present the results of their own research on these issues. The study aims to identify the main indicators of changes in the parameters of mental performance and to assess the risks of professional burnout among therapeutic specialists who actively use information technology. The researchers formed the main sample: 140 specialist doctors (94 women and 46 men) aged 27 to 75 years (average age 46.16±10.67 years)), and divided into two subgroups: the first included 65 respondents (45 women and 20 men) aged 27–75 (average age 44.6±9.8 years) who actively use information and communication technologies in work practice and daily life for less than 6 hours a day, the second subgroup included 75 respondents (49 women and 26 men) 28–73 years old (average age 44.8±9.3 years), actively using information and communication technologies in their work practice and daily life for more than 6 hours a day. To assess mental performance during the working day and week, as well as to study preclinical symptoms of overwork, the authors used: the questionnaire "Degree of chronic fatigue" (A.B. Leonova and I.V. Shishkina; modification — 2003), the method of finding numbers in the Platonov table, a proof-reading test (Bourdon test). The authors also used a visual analyzer with a table (Landolt rings) as a test for assessing throughput. The authors used the following data to assess the risks of emotional burnout: the scale for diagnosing emotional burnout of a person according to V.V. Boyko, the questionnaire for determining the level of mental burnout Maslach (MBI) in the Russian adaptation for medical workers from 2007 at the Bekhterev NIPNI. When analyzing the risks of professional burnout using the MBI questionnaire, the level of emotional exhaustion among respondents using information technology for more than 6 hours a day (43.01±8.7 points) was significantly higher compared to doctors using information technology for less than 6 hours a day (34.5±9.0 points) (Mann–Whitney U test: U=1149, p<0.00001). Indicators of the risk of emotional burnout of a person on the V.V. Boyko scale among internists using information technology for more than 6 hours a day is significantly higher in all three phases: tension, resistance and exhaustion (76,2±15,0; 72,4±14,3; 84,6±15,5 points, respectively) (Mann–Whitney U test: U=1013, p<0.00001; Mann–Whitney U test: U=1302.5, p<0.00001; Mann–Whitney U test: U=1175.5, p<0.00001). The fatigue rates of internists using information technology for more than 6 hours a day are significantly higher compared to the subgroup using CT for less than 6 hours a day (Mann–Whitney U test: U=1618.5, p=0.00062). The vast majority of internists working in hospitals use modern information technologies in their clinical practice, which significantly increases the effectiveness of medical care. Aspects of the influence of computed tomography on the performance of doctors and on the risk of developing professional burnout syndrome (PBS) and psychoemotional disorders (anxiety and depression) have not been fully studied. The study demonstrates the presence of a certain influence of excessive use of digital technologies and electronic devices during the day on the risks of emotional exhaustion and faster development of signs of overwork in therapists. The results of the study can be used to develop and implement measures to prevent the development of signs of overwork and the safe use of information technology in work practice, which to a certain extent reduces the risks of developing professional burnout syndrome, psychoemotional and somatic disorders associated with the inadequate use of modern digital technologies. Limitations. The influence and presence of mild forms of emotional and affective disorders in respondents were not taken into account; the limited sample size associated with the highly specialized focus of the study, it is advisable to increase the sample in the future; the subjectivity of questionnaire research methods, difficulties in assessing the "true" use of information and communication technologies. Ethics. The research was carried out in accordance with the Ethical standards approved by the Helsinki Declaration (1964) and its later additions, or similar Ethical standards. All respondents were familiarized with the goals and objectives of the study, as well as with all possible risks and inconveniences. Participation in the study was voluntary for all respondents, everyone had the right to leave the study at any stage if desired. All personal information provided by the respondents was not disclosed, and all data was depersonalized. All study participants gave their personal written consent to participate in the study.

MMYFnet: Multi-Modality YOLO Fusion Network for Object Detection in Remote Sensing Images

Object detection in remote sensing images is crucial for airport management, hazard prevention, traffic monitoring, and more. The precise ability for object localization and identification enables remote sensing imagery to provide early warnings, mitigate risks, and offer strong support for decision-making processes. While traditional deep learning-based object detection techniques have achieved significant results in single-modal environments, their detection capabilities still encounter challenges when confronted with complex environments, such as adverse weather conditions or situations where objects are obscured. To overcome the limitations of existing fusion methods in terms of complexity and insufficient information utilization, we innovatively propose a Cosine Similarity-based Image Feature Fusion (CSIFF) module and integrate it into a dual-branch YOLOv8 network, constructing a lightweight and efficient target detection network called Multi-Modality YOLO Fusion Network (MMYFNet). This network utilizes cosine similarity to divide the original features into common features and specific features, which are then refined and fused through specific modules. Experimental and analytical results show that MMYFNet performs excellently on both the VEDAI and FLIR datasets, achieving mAP values of 80% and 76.8%, respectively. Further validation through parameter sensitivity experiments, ablation studies, and visual analyses confirms the effectiveness of the CSIFF module. MMYFNet achieves high detection accuracy with fewer parameters, and the CSIFF module, as a plug-and-play module, can be integrated into other CNN-based cross-modality network models, providing a new approach for object detection in remote sensing image fusion.

CT radiation dose reduction with tin filter for localisation/characterisation level image quality in PET-CT: a phantom study

BackgroundThe tin filter has allowed radiation dose reduction in some standalone diagnostic computed tomography (CT) applications. Yet, ‘low-dose’ CT scans are commonly used in positron emission tomography (PET)-CT for lesion localisation/characterisation (L/C), with higher noise tolerated. Thus, dose reductions permissible with the tin filter at this image quality level may differ. The aim was to determine the level of CT dose reduction permitted with the tin filter in PET-CT, for comparable image quality to the clinical reference standard (CRS) L/C CT images acquired with standard filtration.Materials and methodsA whole-body CT phantom was scanned with standard filtration in CRS protocols, using 120 kV with 20mAs-ref for bone L/C (used in 18F-Sodium Fluoride (NaF) PET-CT) and 40mAs-ref for soft tissue L/C (used in 18F-Fluorodeoxyglucose (FDG) PET-CT), followed by tin filter scans at 100 kV (Sn100kV) and 140 kV (Sn140kV) with a range of mAs settings. For each scan, effective dose (ED) in an equivalent-sized patient was calculated, and image quality determined in 5 different tissues through quantitative (contrast-to-noise ratio) and qualitative (visual) analyses. The relative dose reductions which could be achieved with the tin filter for comparable image quality to CRS images were calculated.ResultsQuantitative analysis demonstrated dose savings of 50–76% in bone, 27–51% in lung and 8–61% in soft tissue with use of the tin filter at Sn100kV. Qualitative analysis demonstrated dose reductions using Sn100kV in general agreement with the dose reductions indicated by quantitative analysis. Overall, CT dose reductions of around 85% were indicated for NaF bone PET-CT, allowing whole-body CT at just 0.2mSv ED, and a 30–40% CT dose reduction for FDG PET-CT using Sn100kV (1.7-2.0mSv), providing comparable image quality to current CRS images with standard filtration. Sn140kV demonstrated limited value in CT dose reduction.ConclusionsLarge CT dose reductions can be made using the tin filter at Sn100kV, when imaging bone, lung and soft tissue at L/C level CT image quality in PET-CT. As well as reducing the risk of inducing a cancer in later life, such dose reductions may also impact PET-CT practice, such as justifying cross-sectional over planar imaging or justifying PET-CT in younger patients.

Visualization analysis of literature on sudden poisoning incidents in China from 2013 to 2022 based on CiteSpace and VOSviewer

Objective: To use CiteSpace and VOSviewer software to perform visualization analysis on research literature on sudden poisoning incidents in China from 2013 to 2022, in order to grasp the research hotspots and priorities, current research development status and trend of sudden poisoning incidents, and provide reference and ideas for subsequent research. Methods: In October 2023, literature related to sudden poisoning incidents was searched using the China National Knowledge Infrastructure and Wanfang databases. The bibliometric analysis software CiteSpace 6.1.R2 and VOSviewer 1.6.19 were used to visually analyze factors such as authors, institutions, journals, regions, and keywords in the relevant literature. Results: A total of 2578 valid literatures were included, and the overall number of published literatures showed a discontinuous downward trend in recent 10 years. The relevant literature was distributed in 310 journals. Among the top 10 institutions with the highest number of publications, 8 were centers for disease control and prevention at all levels, and the institution with the highest number of publications was the Chinese Center for Disease Control and Prevention (208 articles), but the cooperation between institutions was not obvious. The 2578 articles involved a total of 6549 authors (a total of 8572 appearances, with an average of 1.31 times per person), with a collaboration rate of 2.54 (6549/2578) and 2153 co-authored articles (with 2 or more authors in the literature), with a co-authorship rate of 83.51%. 643 articles (24.94%) received funding support. The region with the highest number of publications was Guangdong Province, with a total of 380 articles (14.74%). Keyword highlighting analysis showed that the research heat of pesticide poisoning, toxic mushrooms, disease characteristics, outbreak events, traceability analysis, outbreaks, and occupational diseases continued to 2022. Conclusion: There are many literatures on sudden poisoning incidents in China, so it is necessary to strengthen the cooperation and communication between institutions and authors. In practical work, the ability of prevention, control, and detection of poisons should be continuously improved to reduce the incidence and degree of harm caused by sudden poisoning incidents.

EGFDA: Experience-guided Fine-grained Domain Adaptation for cross-domain pneumonia diagnosis

Although recent advances in deep learning have led to accurate pneumonia diagnoses, their heavy reliance on data annotation hinders their expected performance in clinical practice. Unsupervised domain adaptation (UDA) methods have been developed to address the scarcity of annotations. Nevertheless, the diverse manifestations of pneumonia pose challenges for current UDA methods, including spatial lesion-preference bias and discriminative class-preference bias. To overcome these problems, we propose an Experience-Guided Fine-grained Domain Adaptation (EGFDA) framework for automatic cross-domain pneumonia diagnosis. Our framework consists of two main modules: (1) Gradient-aware Lesion Area Matching (GaLAM), which aims to reduce the global domain gap while avoiding misleading from lesion-unrelated targets, and (2) Reweighing Smooth Certainty-aware Matching (RSCaM), which aims to match class space with a smooth certainty-aware feature mapping to guide the model to learn more precise class-discriminative features. Benefiting from the collaboration between GaLAM and RSCaM, the proposed EGFDA is able to process unlabeled samples following a pattern similar to the diagnostic experience of physicians, that is, first locating the disease-related lesion area and then performing fine-grained discrimination. Comprehensive experiments on three different tasks using six datasets demonstrate the superior performance of our EGFDA. Furthermore, extensive ablation studies and visual analyses highlight the remarkable interpretability and generalization of the proposed method.

Privacy-Preserving Detection of Tampered Radio-Frequency Transmissions Utilizing Federated Learning in LoRa Networks.

LoRa networks, widely adopted for low-power, long-range communication in IoT applications, face critical security concerns as radio-frequency transmissions are increasingly vulnerable to tampering. This paper addresses the dual challenges of privacy-preserving detection of tampered transmissions and the identification of unknown attacks in LoRa-based IoT networks. Leveraging Federated Learning (FL), our approach enables the detection of tampered RF transmissions while safeguarding sensitive IoT data, as FL allows model training on distributed devices without sharing raw data. We evaluated the performance of multiple FL-enabled anomaly-detection algorithms, including Convolutional Autoencoder Federated Learning (CAE-FL), Isolation Forest Federated Learning (IF-FL), One-Class Support Vector Machine Federated Learning (OCSVM-FL), Local Outlier Factor Federated Learning (LOF-FL), and K-Means Federated Learning (K-Means-FL). Using metrics such as accuracy, precision, recall, and F1-score, CAE-FL emerged as the top performer, achieving 97.27% accuracy and a balanced precision, recall, and F1-score of 0.97, with IF-FL close behind at 96.84% accuracy. Competitive performance from OCSVM-FL and LOF-FL, along with the comparable results of K-Means-FL, highlighted the robustness of clustering-based detection methods in this context. Visual analyses using confusion matrices and ROC curves provided further insights into each model's effectiveness in detecting tampered signals. This research underscores the capability of federated learning to enhance privacy and security in anomaly detection for LoRa networks, even against unknown attacks, marking a significant advancement in securing IoT communications in sensitive applications.

Improving Persuasive Writing Outcomes With Technology for Students in an Alternative Special Education Program

A study using a multiple-baseline across participants design was conducted in an alternative education setting to examine the functional relation between students’ use of a technology-based graphic organizer (TBGO) with embedded self-regulated learning strategies on the quantity and quality of their persuasive essay writing. Three third- to fifth-grade students with writing difficulties and complex behavioral needs receiving services in an alternative special education program participated in the study. The dependent variables included the number of words, number of transition words, and writing quality scores across (a) baseline (writing on the computer without the TBGO), (b) TBGO-use phase (writing on a computer with the TBGO), and (c) maintenance (writing on the computer without the TBGO). The visual and statistical analyses of data indicated all participants improved the quality of their writing, and two students also increased the quantity of their writing across phases. Students’ self-efficacy for self-regulated learning in writing ratings were also monitored throughout the study. Results indicated an overall increase in their perceived self-efficacy for writing.

Instagram data for validating Nothofagus pumilio distribution mapping in the Southern Andes: A novel ground truthing approach

The availability of valid, non-biased species occurrence data has always been a major challenge for biodiversity research and modelling studies. Data from open-source databases or remote sensing are promising approaches to increase the availability of species occurrence data. However, these data may contain spatial, temporal, and taxonomic biases or require ground truthing. In recent years, social media has received attention for its contribution to species occurrence data sampling and ground truthing approaches. The wide reach of social media platforms allows for rapid and large-scale analyses. Here we introduce a novel Instagram ground truthing approach to validate the occurrence mapping of Nothofagus pumilio across its entire distribution range. The treeline species of the southern Andes has been extensively studied in small-scale studies, but large-scale modelling approaches are largely missing due to limited accessibility to treeline sites resulting in a lack of occurrence data. The content posted on the social media platform Instagram consists of images and videos in which the species N. pumilio and its location can be identified. By searching for suitable posts using hashtags and location tags, we created 1238 Instagram ground truthing points. We compared the performance of our dataset with open-source data from the Global Biodiversity Information Facility (GBIF) through visual, quantitative, and bias analyses, acknowledging that both social media-based and Citizen Science data are subject to sampling and spatial biases due to collection in human-accessible areas. The Instagram ground truthing points were subsequently used to validate remote sensing occurrence data, generated using Sentinel-2 level 2A data and Supervised Classification. The combined approach – Instagram ground truthing and remote sensing – allows for the collection of occurrence data over the entire latitudinal range of N. pumilio, covering approximately 2000 km. The use of social media content provides potentially important contributions to species occurrence data sampling and ground truthing In our study we introduce a novel ground truthing approach for species occurrence data sampling based on Instagram data Instagram ground truthing points, combined with Supervised Classification generate species occurrence data of Nothofagus pumilio over its entire distribution range in the southern Andes The performance of the Instagram ground truthing points is evaluated by comparison with existing data from the GBIF database. Our Instagram ground truthing approach demonstrates a new way of sampling species occurrence data and can be applied to other suitable species and study areas.

Vessel Trajectory Prediction Based on AIS Data: Dual-Path Spatial–Temporal Attention Network with Multi-Attribute Information

With the rapid growth of the global shipping industry, the increasing number of vessels has brought significant challenges to navigation safety and management. Vessel trajectory prediction technology plays a crucial role in route optimization and collision avoidance. However, current prediction methods face limitations when dealing with complex vessel interactions and multi-dimensional attribute information. Most models rely solely on global modeling in the temporal dimension, considering spatial interactions only later, failing to capture dynamic changes in trajectory interactions at different time points. Additionally, these methods do not fully utilize the multi-attribute information in AIS data, and the simple concatenation of attributes limits the model’s potential. To address these issues, this paper proposes a dual spacial–temporal attention network with multi-attribute information (DualSTMA). This network models vessel behavior and interactions through two distinct paths, comprehensively considering individual vessel intentions and dynamic interactions. Moreover, we divide vessel attributes into dynamic and static categories, with dynamic attributes fused during feature preprocessing, and with static attributes being controlled through a gating mechanism during spatial interactions to regulate the importance of neighboring vessel features. Benchmark tests on real AIS data show that DualSTMA significantly outperforms existing methods in prediction accuracy. Ablation studies and visual analyses further validate the model’s reliability and advantages.

Effect of enhanced monofocal intraocular lenses on glaucoma patients' foveal threshold.

We investigated the effect of enhanced monofocal intraocular lenses (IOL) (TECNIS Eyhance DIB00V) on retinal sensitivity in glaucoma patients after cataract surgery by comparing results with of such patients implanted with non-enhanced monofocal intraocular lenses (TECNIS ZCB00V). This was a multi-facility retrospective study of glaucoma patients who had undergone cataract surgery. We measured the value of the foveal threshold with a Humphrey visual field analyzer before and after cataract surgery and considered this value to represent the sensitivity of the retina. As a sub-analysis, we also compared the mean deviation (MD), pattern standard deviation (PSD), thickness of ganglion cell complex (GCC), and best corrected visual acuity (BCVA) before and after surgery. We used t-tests to determine statistical differences between lens groups. We included 60 glaucoma patients (104 eyes) who underwent cataract surgery: enhanced group, n = 58 eyes; non-enhanced group, n = 46 eyes. In both groups, cataract surgery improved the retinal sensitivity (p < 0.001), MD (p < 0.001) and BCVA (p < 0.001). The postoperative foveal thresholds did not significantly differ by lens type (p = 0.673), at 34.41 ± 2.44dB in the enhanced group and 34.61 ± 2.19dB in the non-enhanced group. Likewise, there was no significant difference (p = 0.942) among advanced glaucoma patients, with postoperative foveal thresholds of 33.08 ± 2.35dB and 33.17 ± 3.45dB in the enhanced and non-enhanced, respectively. Implantation of enhanced monofocal IOLs in glaucoma patients did not result in any apparent loss of sensitivity compared with non-enhanced monofocal lens implantation.

Exploring Cybernetics Students' Perceptions of AI in Education: A Comprehensive Analytical Study

This study investigates the multifaceted perceptions of cybernetics students toward integrating artificial intelligence (AI) in educational environments. This research uncovers the students' knowledge levels, preferred sources of information, perceived benefits, and significant students through a comprehensive analysis of survey data combined with recent scholarly insights. Detailed visual analyses underscore key trends, offering a clear perspective on how these students view the future of AI in their academic journey. The findings suggest that while students appreciate AI’s utiAI, they express valid concerns about job security and the potential reduction in human engagement. This study contributes critical insights for educators and policymakers aiming to align AI practices with student needs and expectations.

Surface ablation laser surgery: Bibliometric and visualized analysis from 2004 to 2023.

Surface ablation laser surgery has resurged in popularity recently because of its safety in correcting myopia and favorable postoperative corneal biomechanical properties. This study aimed to investigate the current focal points and future trends in surface ablation laser surgery over the last 2 decades. The Web of Science Core Collection was used as the primary data source to retrieve literature related to surface ablation laser surgery. All records, including full records and reference details, were exported in plain text format. VOSvivewer, CiteSpace, and Pajek were used to perform the bibliometric and visual analyses of the countries/regions, institutions, authors, journals, and keywords of relevant publications. A total of 3415 articles on surface ablation laser surgery were published in 253 journals. These articles were authored by 9681 individuals from 2751 institutions across 79 countries. The United States leads in terms of productivity and influence in this field. The Tehran University of Medical Sciences and Kymion GD were the most productive institutions and authors, whereas the University of Crete and Randleman JB were the most influential. The Journal of Cataract and Refractive Surgery was the most productive and influential in this area, and citation analysis revealed that the top 10 most-cited references focused primarily on postoperative wound healing and wavefront aberration. The keywords were grouped into the following 5 clusters: clinical effects and complications, special indications, iatrogenic corneal ectasia, haze, and pain management. High-frequency keywords in recent years included transepithelial photorefractive keratectomy, retreatment, transepithelial phototherapeutic keratectomy, and biomechanical properties. This bibliometric analysis examined the development trends, global cooperation, research hotspots, and future directions of surface ablation over the past 20 years.

Growth of a Tessellation: Geometric rules for the Development of Stingray Skeletal Patterns.

The skeletons of sharks and rays, fashioned from cartilage, and armored by a veneer of mineralized tiles (tesserae) present a mathematical challenge: How can the continuous covering be maintained as the skeleton expands? This study, using microCT and custom visual data analyses of growing stingray skeletons, systematically examines tessellation patterns and morphologies of the many thousand interacting tesserae covering the hyomandibula (a skeletal element critical to feeding), over a two-fold developmental change in hyomandibula length. The number of tesserae remains surprisingly constant, even as the hyomandibula expands isometrically, with all hyomandibulae displaying self-similar distributions of tesserae shapes/sizes. Although the distribution of tesserae geometries largely agrees with the rules for polyhedra tiling of complex surfaces-dominated by hexagons and a minor fraction of pentagons and heptagons, but very few other polygons-the agreement with Euler's classic mathematical laws is not perfect. Contrary to the assumed uniform growth rate (which is shown would create geometric incompatibilities), larger tesserae grow faster to accommodate skeletal expansion. It is hypothesized that this local regulation of global system complexity is driven by tension (from cartilaginous core expansion) in the fibers connecting tesserae, with strain-responsive cells orchestrating local mineral apposition.

Bidirectional dynamic frame prediction network for total-body [68Ga]Ga-PSMA-11 and [68Ga]Ga-FAPI-04 PET images

PurposeTotal-body dynamic positron emission tomography (PET) imaging with total-body coverage and ultrahigh sensitivity has played an important role in accurate tracer kinetic analyses in physiology, biochemistry, and pharmacology. However, dynamic PET scans typically entail prolonged durations (\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:\\ge\\:$$\\end{document}60 minutes), potentially causing patient discomfort and resulting in artifacts in the final images. Therefore, we propose a dynamic frame prediction method for total-body PET imaging via deep learning technology to reduce the required scanning time.MethodsOn the basis of total-body dynamic PET data acquired from 13 subjects who received [68Ga]Ga-FAPI-04 (68Ga-FAPI) and 24 subjects who received [68Ga]Ga-PSMA-11 (68Ga-PSMA), we propose a bidirectional dynamic frame prediction network that uses the initial and final 10 min of PET imaging data (frames 1–6 and frames 25–30, respectively) as inputs. The peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) were employed as evaluation metrics for an image quality assessment. Moreover, we calculated parametric images (68Ga-FAPI: \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{K}_{1}$$\\end{document}, 68Ga-PSMA: \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{K}_{i}$$\\end{document}) based on the supplemented sequence data to observe the quantitative accuracy of our approach. Regions of interest (ROIs) and statistical analyses were utilized to evaluate the performance of the model.ResultsBoth the visual and quantitative results illustrate the effectiveness of our approach. The generated dynamic PET images yielded PSNRs of 36.056 ± 0.709 dB for the 68Ga-PSMA group and 33.779 ± 0.760 dB for the 68Ga-FAPI group. Additionally, the SSIM reached 0.935 ± 0.006 for the 68Ga-FAPI group and 0.922 ± 0.009 for the 68Ga-PSMA group. By conducting a quantitative analysis on the parametric images, we obtained PSNRs of 36.155 ± 4.813 dB (68Ga-PSMA, \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{K}_{1}$$\\end{document}) and 43.150 ± 4.102 dB (68Ga-FAPI, \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{K}_{i}$$\\end{document}). The obtained SSIM values were 0.932 ± 0.041 (68Ga-PSMA) and 0.980 ± 0.011 (68Ga-FAPI). The ROI analysis conducted on our generated dynamic PET sequences also revealed that our method can accurately predict temporal voxel intensity changes, maintaining overall visual consistency with the ground truth.ConclusionIn this work, we propose a bidirectional dynamic frame prediction network for total-body 68Ga-PSMA and 68Ga-FAPI PET imaging with a reduced scan duration. Visual and quantitative analyses demonstrated that our approach performed well when it was used to predict one-hour dynamic PET images. https://github.com/OPMZZZ/BDF-NET.

Syncopating the Ground Rhythm: W. E. B. Du Bois and Black Geographic Futurity on New Orleans’s North Claiborne Avenue

This research reads New Orleans’s North Claiborne’s 22-block landscape through a Du Boisian cartographic analytic that traces the ground rhythms of white supremacy and the syncopating daily life of Blacks residents’ resistance to racial oppression. Drawing on Du Bois’s The Philadelphia Negro as well as his work of speculative fiction, The Princess Steel, and cartographic experiments for the 1900 Paris World Exposition, and through textual and visual analyses of this critical Black geography, I argue that Du Boisian spatial analytics continue to be critical to how Black futures and freedom might be imagined. This Du Boisian analytic tends to archival and canonical absences; insists on the temporal, relational, and geographical dimensions of white supremacy; challenges the flattening of racial classificatory systems and cartographic logics; and represents heterogeneity as a rich and complex genealogical intermix of resistance across time and space. By reading North Claiborne for its geographical and temporal synergies, I draw on Du Bois to empirically grapple with and represent a capacious Black geographical imaginary and grounded praxis that blurs geographical and temporal delineations, contrasts white supremacy with freedom as a geographical praxis, and transposes abstract cartographies with Black life and placemaking.