Exploring Contact Distance Distributions with Google Colaboratory.

Hi-C and Micro-C are the three-dimensional (3D) genome assays that use high-throughput sequencing. In the analysis, the sequenced paired-end reads are mapped to a reference genome to generate a two-dimensional contact matrix for identifying topologically associating domains (TADs), chromatin loops, and chromosomal compartments. On the other hand, the distance distribution of the paired-end mapped reads also provides insight into the 3D genome structure by highlighting global contact frequency patterns at distances indicative of loops, TADs, and compartments. This chapter presents a basic workflow for visualizing and analyzing contact distance distributions from Hi-C data. The workflow can be run on Google Colaboratory, which provides a ready-to-use Python environment accessible through a web browser. The notebook that demonstrates the workflow is available in the GitHub repository at https://github.com/rnakato/Springer_contact_distance_plot.

Analysis and Visualization of Multiple Hi-C and Micro-C Data with CustardPy.

Three-dimensional (3D) genome structure plays crucial roles in biological processes and disease pathogenesis. Hi-C and Micro-C, well-established methods for 3D genome analysis, can identify a variety of 3D genome structures. However, selecting appropriate pipelines and tools for the analysis and setting up the required computing environment can sometimes pose challenges. To address this, we have introduced CustardPy, a Docker-based pipeline specifically designed for 3D genome analysis. CustardPy is designed to compare and evaluate multiple samples and wraps several existing tools to cover the entire workflow from FASTQ mapping to visualization. In this chapter, we demonstrate how to analyze and visualize Hi-C data using CustardPy and introduce several 3D genome features observed in Hi-C data.

Processing and Visualization of Protec-Seq Data for the Generation of Calibrated, Genome-Wide Double Double-Strand Break (dDSB) Maps.

This chapter describes the computational pipeline for the processing and visualization of Protec-Seq data, a method for purification and genome-wide mapping of double-stranded DNA protected by a specific protein at both ends. In the published case, the protein of choice was Saccharomyces cerevisiae Spo11, a conserved topoisomerase-like enzyme that makes meiotic double-strand breaks (DSBs) to initiate homologous recombination, ensuring proper segregation of homologous chromosomes and fertility. The isolated DNA molecules were thus termed double DSB (dDSB) fragments and were found to represent 34 to several hundred base-pair long segments that are generated by Spo11 and are enriched at DSB hotspots, which are sites of topological stress. In order to allow quantitative comparisons between dDSB profiles across experiments, we implemented calibrated chromatin immunoprecipitation sequencing (ChIP-Seq) using the meiosis-competent yeast species Saccharomyces kudriavzevii as calibration strain. Here, we provide a detailed description of the computational methods for processing, analyzing, and visualizing Protec-Seq data, comprising the download of the raw data, the calibrated genome-wide alignments, and the scripted creation of either arc plots or Hi-C-style heatmaps for the illustration of chromosomal regions of interest. The workflow is based on Linux shell scripts (including wrappers for publicly available, open-source software) as well as R scripts and is highly customizable through its modular structure.

Research on aerobic fitness in children and adolescents: a bibliometric analysis based on the 100 most-cited articles.

Introduction: This study aims to conduct a bibliometric analysis of the 100 most-cited articles to examine research trends, hot topics, and gaps in aerobic fitness research in children and adolescents, addressing the lack of evidence synthesis. Methods: The Web of Science Core Collection database was used for literature search, and bibliometric characteristics of the included research articles were imported and calculated. Descriptive statistics and visualizations by the VOS viewer were used for the presentation of bibliometric characteristics. Results: The 100 most cited articles received an average of 104 citations. British Journal of Sports Medicine and Medicine and Science in Sports and Exercise were the two top journals that published aerobic fitness research in children and adolescents. The United States was the top country that contributed to the most-cited research articles. Three top research topics were identified from the analysis, such as neuroscience, developmental psychology, and aerobic health. Conclusion: Aerobic fitness research in children and adolescents has received much attention and interest since 2000. The most contributing authors in this research field were from developed countries, such as the United States, and cognition and health-related research were priorities.

Free tools for crystallographic symmetry handling and visualization.

Online courses and innovative teaching methods have triggered a trend in education, where the integration of multimedia, online resources and interactive tools is reshaping the view of both virtual and traditional classrooms. The use of interactive tools extends beyond the boundaries of the physical classroom, offering students the flexibility to access materials at their own speed and convenience and enhancing their learning experience. In the field of crystallography, there are a wide variety of free online resources such as web pages, interactive applets, databases and programs that can be implemented in fundamental crystallography courses for different academic levels and curricula. This paper discusses a variety of resources that can be helpful for crystallographic symmetry handling and visualization, discussing four specific resources in detail: the Bilbao Crystallographic Server, the Cambridge Structural Database, VESTA and Jmol. The utility of these resources is explained and shown by several illustrative examples.

Using BioRender for Active Learning: Exploring Learning-Style Preference and Visual-Spatial Ability in Undergraduate Students.

Visual-spatial reasoning has been considered a predictor of performance success in STEM courses, including engineering, chemistry, biology, and mathematics. Little is known, however, about whether visual-spatial ability predicts success for non-STEM students in general education neuroscience courses. In the following study, we investigate how scores on tests of visual-spatial object rotation relate to student performance on illustrative and content exams in a large non-major undergraduate neuropharmacology course. To help students understand content visually, the course provided students with homework assignments that allowed them to create illustrations of lecture content using the online scientific illustration software, BioRender. Findings suggest that percent completion of BioRender assignments was a greater predictor of student performance than tests of innate visual-spatial ability. In addition, we show that visual learning style preference was not correlated with visual-spatial ability, as measured by the Purdue Spatial Visualization Test-Visualization of Rotations. Neither did learning style preference predict student success. The following paper suggests practice illustrating neuroscience concepts, or perhaps content practice in general, had a greater impact on student learning independent of learning style preference or innate visual-spatial ability.

Pixel-wise annotation for clear and contaminated regions segmentation in wireless capsule endoscopy images: A multicentre database.

Wireless capsule endoscopy (WCE) is capable of non-invasively visualizing the small intestine, the most complicated segment of the gastrointestinal tract, to detect different types of abnormalities. However, its main drawback is reviewing the vast number of captured images (more than 50,000 frames). The recorded images are only sometimes clear, and different contaminating agents, such as turbid materials and air bubbles, degrade the visualization quality of the WCE images. This condition could cause serious problems such as reducing mucosal view visualization, prolonging recorded video reviewing time, and increasing the risks of missing pathology. On the other hand, accurately quantifying the amount of turbid fluids and bubbles can indicate potential motility malfunction. To assist in developing computer vision-based techniques, we have constructed the first multicentre publicly available clear and contaminated annotated dataset by precisely segmenting 17,593 capsule endoscopy images from three different databases. In contrast to the existing datasets, our dataset has been annotated at the pixel level, discriminating the clear and contaminated regions and subsequently differentiating bubbles and turbid fluids from normal tissue. To create the dataset, we first selected all of the images (2906 frames) in the reduced mucosal view class covering different levels of contamination and randomly selected 12,237 images from the normal class of the copyright-free CC BY 4.0 licensed small bowel capsule endoscopy (SBCE) images from the Kvasir capsule endoscopy database. To mitigate the possible available bias in the mentioned dataset and to increase the sample size, the number of 2077 and 373 images have been stochastically chosen from the SEE-AI project and CECleanliness datasets respectively for the subsequent annotation. Randomly selected images have been annotated with the aid of ImageJ and ITK-SNAP software under the supervision of an expert SBCE reader with extensive experience in gastroenterology and endoscopy. For each image, two binary and tri-colour ground truth (GT) masks have been created in which each pixel has been indexed into two classes (clear and contaminated) and three classes (bubble, turbid fluids, and normal), respectively. To the best of the author's knowledge, there is no implemented clear and contaminated region segmentation on the capsule endoscopy reading software. Curated multicentre dataset can be utilized to implement applicable segmentation algorithms for identification of clear and contaminated regions and discrimination bubbles, as well as turbid fluids from normal tissue in the small intestine. Since the annotated images belong to three different sources, they provide a diverse representation of the clear and contaminated patterns in the WCE images. This diversity is valuable for training the models that are more robust to variations in data characteristics and can generalize well across different subjects and settings. The inclusion of images from three different centres allows for robust cross-validation opportunities, where computer vision-based models can be trained on one centre's annotated images and evaluated on others.

Development of a visual detection method of porcine deltacoronavirus using loop-mediated isothermal amplification.

The emergence of porcine deltacoronavirus (PDCoV) presents a significant threat to both human and animal health due to its ability to cause highly contagious enteric diseases. This underscores the crucial need for timely and accurate diagnosis to facilitate effective epidemiological investigation and clinical management. This research aimed to establish a visual detection method based on reverse transcription loop-mediated isothermal amplification (RT-LAMP) for PDCoV testing. In this study, six pairs of primers were designed according to the conserved sequences of PDCoV ORF1a/b genes. The primer sets and parameters that affect LAMP reaction were optimized. The visual RT-LAMP method was developed by incorporating methyl red into the optimized reaction system, it exclusively detected PDCoV without cross-reactivity with other viruses and the detection limits for PDCoV could reach 10 copies/µL. In comparison with RT-PCR for testing 132 clinical samples, the relative specificity and sensitivity of the visual RT-LAMP were found to be 99.2 and 100%, respectively, with a concordance rate of 99.2% and a kappa value of 0.959, indicating that the visual RT-LAMP is a reliable method for the application of PDCoV detection in clinical samples.

The value of non-enhanced CT 3D visualization in differentiating stage â invasive lung adenocarcinoma between LPA and non-LPA.

Objective: This study aims to analyze the quantitative parameters and morphological indices of three-dimensional (3D) visualization to differentiate lepidic predominant adenocarcinoma (LPA) from non-LPA subtypes, which include acinar predominant adenocarcinoma (APA), papillary predominant adenocarcinoma (PPA), micropapillary predominant adenocarcinoma (MPA), and solid predominant adenocarcinoma (SPA). Methods: A group of 178 individuals diagnosed with lung adenocarcinoma were chosen and categorized into two groups: the LPA group and the non-LPA group, according to their pathological results. Quantitative parameters and morphological indexes such as 3D volume, solid proportion, and vascular cluster sign were obtained using 3D visualization and reconstruction techniques. Results: Significant differences were observed in the vascular cluster sign, spiculation, shape, air bronchogram, bubble-like lucency, margin, pleural indentation, lobulation, maximum tumor diameter, 3D mean CT value, 3D volume, 3D mass, 3D density, and solid proportion between two groups (P<0.05). The optimal cut-off values for diagnosing non-LPA were a 3D mean CT value of -445.45 HU, a 3D density of 0.56 mg·mm-3, and a solid proportion reaching 27.95 %. Multivariate logistic regression analysis revealed that 3D mean CT value, lobulation, and margin characteristics independently predicted stageⅠinvasive lung adenocarcinoma. The combination of three indicators significantly improved prediction accuracy (AUC=0.881). Conclusion: The utilization of 3D visualization technology in a systematic approach enables the acquisition of 3D quantitative parameters and morphological indicators of thin-slice CT lesions. These efforts significantly contribute to the identification of histopathological subtypes for stageⅠinvasive lung adenocarcinoma. When integrated with pertinent clinical data, this offers essential guidance for developing various surgical techniques and treatment plans.

Global research trends in postoperative ileus from 2011 to 2023: A scientometric study.

BACKGROUND: Postoperative ileus (POI) is a common complication after abdominal surgery with high morbidity, which hinders patient recovery, prolongs hospitalization, and increases healthcare costs. Therefore, POI has become a global public health challenge. POI triggering is multifactorial. Autonomic and hormonal mechanisms are generally involved in POI pathogenesis. Recent studies have shown that beta adrenergic signaling of enteric glia is a POI trigger. Currently, the status quo, trends, and frontiers of global research on POI remain unclear. AIM: To explore the current status, trends, and frontiers of POI research from 2011 to the present based on bibliometric analysis. METHODS: Publications published on POI research from 2011 to 2023 were retrieved on June 1, 2023, from the Web of Science Core Collection. CiteSpace 6.2.R2 and VOSviewer were used to conduct bibliometric visualization. RESULTS: In total, 778 POI records published from 2011 to 2023 were retrieved. Over the past few decades, the annual cumulative number of related articles has linearly increased, with China and the United States of America contributing prominently. All publications were from 59 countries and territories. China and the University of Bonn were the top contributing country and institution, respectively. Neurogastroenterology & Motility was the most prolific journal. The Journal of Gastrointestinal Surgery had the highest number of citations. Wehner Sven was the most productive author. Burst keywords (e.g., colon, prolonged ileus, acupuncture, paralytic ileus, pathophysiology, rectal cancer, gastrointestinal function, risk) and a series of reference citation bursts provided evidence for the research frontiers in recent years. CONCLUSION: This study demonstrates trends in the published literature on POI and provides new insights for researchers. It emphasizes the importance of multidisciplinary cooperation in the development of this field.

Research status and hotspots in transjugular intrahepatic portosystemic shunts based on CiteSpace bibliometric analysis.

BACKGROUND: The transjugular intrahepatic portosystemic shunt (TIPS) is an important technique for treating complications related to portal hypertension in patients with cirrhosis, and the number of publications in the TIPS field continues to rise. AIM: To facilitate an understanding of the research status and hotspots in the field of TIPS using CiteSpace bibliometric analysis. METHODS: CiteSpace is a software that depicts the strength of relationships through graphics and connections with diverse functionalities and can be used to analyze the status and hotspots of areas of research. Articles on TIPS in the Web of Science Core Collection were retrieved, and CiteSpace software was used to visualize and analyze the number of publications, journals, countries, institutions, authors, keywords, and citations. RESULTS: A total of 985 relevant documents were included in the analysis. From January 2013 to December 2022, the number of publications increased annually. The journal, institution, and author with the greatest number of publications in the field of TIPS are the Journal of Vascular and Interventional Radiology, the University of Bonn, and Jonel Trebicka, respectively. The main keywords used in this field are "transjugular intrahepatic portosystemic shunt", "portal hypertension", "cirrhosis", "management", "stent", "hepatic encephalopathy", "refractory ascite", "survival", "risk", and "variceal bleeding". The greatest obstacle to TIPS placement is currently the occurrence of hepatic encephalopathy. The research hotspots are the mechanism, risk factors, management, and control of hepatic encephalopathy. CONCLUSION: This bibliometric analysis reported the research status and hotspots of TIPS. Research on postoperative hepatic encephalopathy is the research hotspot in this field.

Mapping new psychoactive substances: Leveraging GIS technology for advanced global surveillance and policy support.

The escalating challenge of New Psychoactive Substances (NPS) necessitates enhanced global monitoring and analysis capabilities. This study introduces an advanced interactive visualization tool that employs Geographic Information System (GIS) technologies to improve the functionality of the UNODC's Early Warning Advisory. The tool enables dynamic observation and analysis of NPS's geographical and temporal distribution, thereby facilitating a comprehensive understanding of their public health impacts. By incorporating detailed choropleth maps and annual and cumulative bar charts, the tool allows policymakers and researchers to visually track and analyze trends in NPS usage and control efforts across different regions. The results demonstrate the tool's effectiveness in providing actionable insights, which support the strategic development of public health policies and interventions to curb the global rise in NPS usage. This initiative illustrates the essential role of digital tools in enhancing public health strategies and responses to emerging drug trends. This rising challenge underscores the urgent need for innovative solutions in monitoring drug trends, a theme explored in this paper. The web tool is available at https://nps-vis.cmdm.tw, and the code is available at https://github.com/CMDM-Lab/nps-vis.

Network Analysis to Visualize Qualitative Results: Example From a Qualitative Content Analysis of The National Child Abuse Hotline.

Data visualization, such as figures created through network analysis, may be one way to present more complete information from qualitative analysis. Segments of qualitatively coded data can be treated as objects in network analysis, thus creating visual representations of the code frequency (i.e., nodes) and the co-occurrence (i.e., edges). By sharing an example of network analysis applied to qualitative data, and then comparing our process with other applications, our goal is to help other researchers reflect on how this approach may support their interpretation and visualization of qualitative data. A total of 265 de-identified transcripts between help-seekers and National Child Abuse Hotline crisis counselors were included in the network analysis. Post-conversation surveys, including help-seekers' perceptions of the conversations, were also included in the analysis. Qualitative content analysis was conducted, which was quantified as the presence or absence of each code within a transcript. Then, we divided the dataset based on help-seekers' perceptions. Individuals who responded that they "Yes/Maybe" felt more hopeful after the conversation were in the "hopeful" dataset, while those who answered "No" were in the "unhopeful" dataset. This information was imported to UCINET to create co-occurrence matrices. Gephi was used to visualize the network. Overall, code co-occurrence networks in hopeful conversations were denser. Furthermore, the average degree was higher in these hopeful conversations, suggesting more codes were consistently present. Codes in hopeful conversations included information, counselor support, and problem-solving. Conversely, non-hopeful conversations focused on information. Overall, network analysis revealed patterns that were not evident through traditional qualitative analysis.

Optimization of hybridization chain reaction for imaging single RNA molecules in Drosophila larvae.

In situ hybridization techniques are powerful methods for exploring gene expression in a wide range of biological contexts, providing spatial information that is most often lost in traditional biochemical techniques. However, many in situ hybridization methods are costly and time-inefficient, particularly for screening-based projects that follow on from single-cell RNA sequencing data, which rely on of tens of custom-synthetized probes against each specific RNA of interest. Here we provide an optimized pipeline for Hybridization Chain Reaction (HCR)-based RNA visualization, including an open-source code for optimized probe design. Our method achieves high specificity and sensitivity with the option of multiplexing using only five pairs of probes, which greatly lowers the cost and time of the experiment. These features of our HCR protocol are particularly useful and convenient for projects involving screening several genes at medium throughput, especially as the method include an amplification step, which makes the signal readily visible at low magnification imaging.

Improved yolov5 algorithm combined with depth camera and embedded system for blind indoor visual assistance.

To assist the visually impaired in their daily lives and solve the problems associated with poor portability, high hardware costs, and environmental susceptibility of indoor object-finding aids for the visually impaired, an improved YOLOv5 algorithm was proposed. It was combined with a RealSense D435i depth camera and a voice system to realise an indoor object-finding device for the visually impaired using a Raspberry Pi 4 B device as its core. The algorithm uses GhostNet instead of the YOLOv5s backbone network to reduce the number of parameters and computation of the model, incorporates an attention mechanism (coordinate attention), and replaces the YOLOv5 neck network with a bidirectional feature pyramid network to enhance feature extraction. Compared to the YOLOv5 model, the model size was reduced by 42.4%, number of parameters was reduced by 47.9%, and recall rate increased by 1.2% with the same precision. This study applied the improved YOLOv5 algorithm to an indoor object-finding device for the visually impaired, where the searched object was input by voice, and the RealSense D435i was used to acquire RGB and depth images to realize the detection and ranging of the object, broadcast the specific distance of the target object by voice, and assist the visually impaired in finding the object.

Clinical Outcomes of Full-Endoscopic Visualized Foraminoplasty and Discectomy for Lumbar Disc Herniation with Bilateral Radiculopathy.

OBJECTIVE: The treatment of lumbar disc herniation (LDH) with bilateral radiculopathy using transforaminal endoscopic lumbar discectomy (TELD) remains challenging, especially at the L5/S1 level with narrow foramen or high iliac crest. Full-endoscopic visualized foraminoplasty and discectomy (FEVFD) is a newly developed technique for LDH and lumbar stenosis. However, there is limited evidence on the efficacy of FEVFD technique in the treatment of LDH with bilateral radiculopathy. This study was to assess the clinical outcomes and safety of using FEVFD in the treatment of LDH with bilateral radiculopathy. METHODS: This retrospective study enrolled 63 patients with LDH presenting with bilateral radiculopathy between January 2018 and January 2022. Patients enrolled before January 2020 were treated using a conventional transforaminal endoscopic surgical system (TESSYS) technique (TESSYS, n = 33) and treated using a FEVFD technique after that (FEVFD, n = 30). The total operation time and the number of intraoperative fluoroscopies were recorded. The Oswestry Disability Index (ODI) and visual analog scale (VAS) were evaluated preoperatively and postoperatively (at 1-month, 3-month, 6-month, and final follow-ups). Global outcomes at final follow-up were assessed using modified MacNab criteria. RESULTS: Compared with TESSYS, patients in FEVFD group had a shorter operation time (92.9 vs. 78.0 min). The intraoperative fluoroscopies in FEVFD group were significantly lower than those in TESSYS group (18.7 vs. 4.9). After the operation, the VAS and ODI scores at all follow-ups in the two groups were significantly lower than those before operation. For the L5/S1 level, the values of VAS and ODI scores in FEVFD group were significantly lower than those of in TESSYS group at 3-month, 6-month, and final follow-up. For the L4/5 level, however, no significant difference was found in VAS and ODI scores between these two groups at the follow-ups. According to the modified MacNab criteria, the excellent-to-good rate in TESSYS and FEVFD groups was 84.8% and 90.0%, respectively. CONCLUSION: For LDH with bilateral radiculopathy, using the FEVFD technique could not only reduce the operation time and radiation, but also improve the clinical outcomes at the L5/S1 level.

From past to digital time: Bibliometric perspective of worldwide research productivity on robotic and computer-assisted arthroplasty.

Objective: The number of citations can be used as an impact marker of research work. This study aimed to determine and characterize the worldwide research productivity on robotic and computer-assisted arthroplasty. Methods: All accessible publications from 1992 to 2023 on robotic and computer-assisted arthroplasty from Web of Science Core Collection (WOSCC) database were recorded in August 2024. The following aspects were retrieved: cited times, name of author, keywords, institution, country, year of publication, journal, title, topic, impact factor, and H-index. VOSviewer software and Microsoft Excel were conducted to make the bibliometric research visual. The nature of our study is a systematic study and was conducted in China. Results: 1061 articles were included in our study. The total cited times were 27,461 with the average number of 26. The most productive year was 2022, with a total of 158 publications. The United States contributed the highest number of articles (n = 389, 36.66%) and the Hospital for Special Surgery (n = 53, 5.00%) held the leading institution. "Orthopedics" became the dominant topic (n = 894, 84.26%) and the latest keywords "clinical outcomes", "acetabular cup placement", and "satisfaction" have mainly appeared since 2020. Conclusions: Our analysis gives a comprehensive review of related articles on robotic and computer-assisted arthroplasty from past to future. The United States dominated studies of robotic and computer-assisted arthroplasty and a journal about arthroplasty was the most productive one. "Clinical outcomes", "Acetabular cup placement", and "Satisfaction" may become the future research hotspots.

Method to apply temporal graph analysis on electronic patient record data to explore healthcare professional-patient interaction intensity: a cohort study.

AIM: Interactions between patients and healthcare professionals (HCP) during hospital admissions are complex and difficult to interrogate using traditional analysis of electronic patient record (EPR) data. The objective of this study was to determine the feasibility of applying temporal network analytics to EPR data, focusing on HCP-patient interactions over time. METHOD: Network (graph) analysis was applied to routinely collected structured data from an EPR for HCP interactions with individual patients during admissions for patients undergoing renal transplantation between May 2019 and June 2023. Networks were constructed per day of admission within a session, defined by whether the patient was in the intensive care unit (ICU) or standard hospital ward. Connections between HCP were defined using a 60 min period. Reports were generated visualising daily interaction network structures, across individual admissions. RESULTS: 2300 individual networks were constructed from 127 hospital admissions for renal transplantation. The number of nodes or HCP per network varied from 2 to 45, and network metrics provided detail regarding variation in the density and transitivity, changes in structure with different diameters and radii, and variations in centralisation. Each network analysis metric has a contribution to play in describing the dynamics of a daily HCP network and the composite findings provide insights that cannot be determined with standard approaches. CONCLUSIONS: Network analysis provides a novel approach to investigate and visualise patterns of HCP-patient interactions which allow for a deeper understanding of the complex nature of hospital patient care and could have numerous practical operational applications.

GP-Plotter: Flexible Spectral Visualization for Proteomics Data with Emphasis on Glycoproteomics Analysis.

Identification evaluation and result dissemination are essential components in mass spectrometry-based proteomics analysis. The visualization of fragment ions in mass spectrum provides strong evidence for peptide identification and modification localization. Here, we present an easy-to-use tool, named GP-Plotter, for ion annotation of tandem mass spectra and corresponding image output. Identification result files of common searching tools in the community and user-customized files are supported as input of GP-Plotter. Multiple display modes and parameter customization can be achieved in GP-Plotter to present annotated spectra of interest. Different image formats, especially vector graphic formats, are available for image generation which is favorable for data publication. Notably, GP-Plotter is also well-suited for the visualization and evaluation of glycopeptide spectrum assignments with comprehensive annotation of glycan fragment ions. With a user-friendly graphical interface, GP-Plotter is expected to be a universal visualization tool for the community. GP-Plotter has been implemented in the latest version of Glyco-Decipher (v1.0.4) and the standalone GP-Plotter software is also freely available at https://github.com/DICP-1809.