scholarly journals E-scape: Interactive visualization of single cell phylogenetics and spatio-temporal evolution in cancer

2016 ◽  
Author(s):  
Maia A. Smith ◽  
Cydney Nielsen ◽  
Fong Chun Chan ◽  
Andrew McPherson ◽  
Andrew Roth ◽  
...  
Keyword(s):  
Single Cell ◽  
Visual Analytics ◽  
Domain Knowledge ◽  
Treatment Resistance ◽  
Cancer Evolution ◽  
Imaging Data ◽  
Web Based ◽  
Clinical Endpoints ◽  
Cell Experiment ◽  
Spatio Temporal

Inference of clonal dynamics and tumour evolution has fundamental importance in understanding the major clinical endpoints in cancer: development of treatment resistance, relapse and metastasis. DNA sequencing technology has made measuring clonal dynamics through mutation analysis accessible at scale, facilitating computational inference of informative patterns of interest. However, currently no tools allow for biomedical experts to meaningfully interact with the often complex and voluminous dataset to inject domain knowledge into the inference process. We developed an interactive, web-based visual analytics software suite called E-scape which supports dynamically linked, multi-faceted views of cancer evolution data. Developed using R and javascript d3.js libraries, the suite includes three tools: TimeScape and MapScape for visualizing population dynamics over time and space, respectively, and CellScape for visualizing evolution at single cell resolution. The tool suite integrates phylogenetic, clonal prevalence, mutation and imaging data to generate intuitive, dynamically linked views of data which update in real time as a function of user actions. The system supports visualization of both point mutation and copy number alterations, rendering how mutations distribute in clones in both bulk and single cell experiment data in multiple representations including phylogenies, heatmaps, growth trajectories, spatial distributions and mutation tables. E-scape is open source and is freely available to the community at large.

scholarly journals A Framework for Visual Analytics of Spatio-Temporal Sensor Observations from Data Streams

2018 ◽  
Vol 7 (12) ◽  
pp. 475 ◽  
Author(s):  
Bolelang H Sibolla ◽  
Serena Coetzee ◽  
Terence L Van Zyl
Keyword(s):  
Data Streams ◽  
High Frequency ◽  
Visual Analytics ◽  
Domain Knowledge ◽  
User Model ◽  
Streaming Data ◽  
Design Model ◽  
Observation Data ◽  
Spatio Temporal ◽  
Frequency Changes

Sensor networks generate substantial amounts of frequently updated, highly dynamic data that are transmitted as packets in a data stream. The high frequency and continuous unbound nature of data streams leads to challenges when deriving knowledge from the underlying observations. This paper presents (1) a state of the art review into visual analytics of geospatial, spatio-temporal streaming data, and (2) proposes a framework based on the identified gaps from the review. The framework consists of (1) the data model that characterizes the sensor observation data, (2) the user model, which addresses the user queries and manages domain knowledge, (3) the design model, which handles the patterns that can be uncovered from the data and corresponding visualizations, and (4) the visualization model, which handles the rendering of the data. The conclusion from the visualization model is that streaming sensor observations require tools that can handle multivariate, multiscale, and time series displays. The design model reveals that the most useful patterns are those that show relationships, anomalies, and aggregations of the data. The user model highlights the need for handling missing data, dealing with high frequency changes, as well as the ability to review retrospective changes.

scholarly journals Templated Text Synthesis for Expert-Guided Multi-Label Extraction from Radiology Reports

2021 ◽  
Vol 3 (2) ◽  
pp. 299-317
Author(s):  
Patrick Schrempf ◽  
Hannah Watson ◽  
Eunsoo Park ◽  
Maciej Pajak ◽  
Hamish MacKinnon ◽  
...  
Keyword(s):  
Domain Knowledge ◽  
Expert Knowledge ◽  
Medical Image Analysis ◽  
Imaging Data ◽  
Data Synthesis ◽  
Domain Specific ◽  
Radiology Reports ◽  
Training Examples ◽  
Analysis Models ◽  
Difficult Cases

Training medical image analysis models traditionally requires large amounts of expertly annotated imaging data which is time-consuming and expensive to obtain. One solution is to automatically extract scan-level labels from radiology reports. Previously, we showed that, by extending BERT with a per-label attention mechanism, we can train a single model to perform automatic extraction of many labels in parallel. However, if we rely on pure data-driven learning, the model sometimes fails to learn critical features or learns the correct answer via simplistic heuristics (e.g., that “likely” indicates positivity), and thus fails to generalise to rarer cases which have not been learned or where the heuristics break down (e.g., “likely represents prominent VR space or lacunar infarct” which indicates uncertainty over two differential diagnoses). In this work, we propose template creation for data synthesis, which enables us to inject expert knowledge about unseen entities from medical ontologies, and to teach the model rules on how to label difficult cases, by producing relevant training examples. Using this technique alongside domain-specific pre-training for our underlying BERT architecture i.e., PubMedBERT, we improve F1 micro from 0.903 to 0.939 and F1 macro from 0.512 to 0.737 on an independent test set for 33 labels in head CT reports for stroke patients. Our methodology offers a practical way to combine domain knowledge with machine learning for text classification tasks.

scholarly journals Melanoma Single-Cell Biology in Experimental and Clinical Settings

2021 ◽  
Vol 10 (3) ◽  
pp. 506
Author(s):  
Hans Binder ◽  
Maria Schmidt ◽  
Henry Loeffler-Wirth ◽  
Lena Suenke Mortensen ◽  
Manfred Kunz
Keyword(s):  
T Cell ◽  
Single Cell ◽  
Intracellular Signaling ◽  
Cell Biology ◽  
Immune Cell ◽  
Malignant Tumors ◽  
Checkpoint Inhibitor ◽  
Treatment Resistance ◽  
Resistance Mechanisms ◽  
Cellular Heterogeneity

Cellular heterogeneity is regarded as a major factor for treatment response and resistance in a variety of malignant tumors, including malignant melanoma. More recent developments of single-cell sequencing technology provided deeper insights into this phenomenon. Single-cell data were used to identify prognostic subtypes of melanoma tumors, with a special emphasis on immune cells and fibroblasts in the tumor microenvironment. Moreover, treatment resistance to checkpoint inhibitor therapy has been shown to be associated with a set of differentially expressed immune cell signatures unraveling new targetable intracellular signaling pathways. Characterization of T cell states under checkpoint inhibitor treatment showed that exhausted CD8+ T cell types in melanoma lesions still have a high proliferative index. Other studies identified treatment resistance mechanisms to targeted treatment against the mutated BRAF serine/threonine protein kinase including repression of the melanoma differentiation gene microphthalmia-associated transcription factor (MITF) and induction of AXL receptor tyrosine kinase. Interestingly, treatment resistance mechanisms not only included selection processes of pre-existing subclones but also transition between different states of gene expression. Taken together, single-cell technology has provided deeper insights into melanoma biology and has put forward our understanding of the role of tumor heterogeneity and transcriptional plasticity, which may impact on innovative clinical trial designs and experimental approaches.

scholarly journals Circulating tumor cell heterogeneity in neuroendocrine prostate cancer by single cell copy number analysis

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Vincenza Conteduca ◽  
Sheng-Yu Ku ◽  
Luisa Fernandez ◽  
Angel Dago-Rodriquez ◽  
Jerry Lee ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Single Cell ◽  
De Novo ◽  
Genomic Analysis ◽  
Treatment Resistance ◽  
Circulating Tumor Cell ◽  
Prostate Adenocarcinoma ◽  
Neuroendocrine Prostate Cancer ◽  
Patient Heterogeneity ◽  
Tumor Cell Heterogeneity

AbstractNeuroendocrine prostate cancer is an aggressive variant of prostate cancer that may arise de novo or develop from pre-existing prostate adenocarcinoma as a mechanism of treatment resistance. The combined loss of tumor suppressors RB1, TP53, and PTEN are frequent in NEPC but also present in a subset of prostate adenocarcinomas. Most clinical and preclinical studies support a trans-differentiation process, whereby NEPC arises clonally from a prostate adenocarcinoma precursor during the course of treatment resistance. Here we highlight a case of NEPC with significant intra-patient heterogeneity observed across metastases. We further demonstrate how single-cell genomic analysis of circulating tumor cells combined with a phenotypic evaluation of cellular diversity can be considered as a window into tumor heterogeneity in patients with advanced prostate cancer.

Evaluation of Geuenich et al.: Targeting a crucial bottleneck for analyzing single-cell multiplexed imaging data

Cell Systems ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1121-1123
Author(s):  
Inna Averbukh ◽  
Noah F. Greenwald ◽  
Candace C. Liu ◽  
Michael Angelo
Keyword(s):  
Single Cell ◽  
Imaging Data ◽  
Multiplexed Imaging

Live-action mass-casualty training and virtual world training

2016 ◽  
Vol 60 (1) ◽  
pp. 2103-2107 ◽  
Author(s):  
Keith T. Shubeck ◽  
Scotty D. Craig ◽  
Xiangen Hu
Keyword(s):  
Virtual World ◽  
Domain Knowledge ◽  
Mass Casualty ◽  
Learning Technologies ◽  
Learning Needs ◽  
Web Based ◽  
Tutoring Systems ◽  
Aeromedical Evacuation ◽  
User Input ◽  
Live Action

Live-action training simulations with expert facilitators are considered by many to be the gold-standard in training environments. However, these training environments are expensive, provide many logistical challenges, and may not address the individual’s learning needs. Fortunately, advances in distance-based learning technologies have provided the foundation for inexpensive and effective learning environments that can simultaneously train and educate students on a much broader scale than live-action training environments. Specifically, intelligent tutoring systems (ITSs) have been proven to be very effective in improving learning outcomes. The Virtual Civilian Aeromedical Evacuation Sustainment Training (VCAEST) interface takes advantage of both of these technologies by enhancing a virtual world with a web-based ITS, AutoTutor LITE (Learning in Interactive Training Environments). AutoTutor LITE acts as a facilitator in the virtual world by providing just-in-time feedback, presenting essential domain knowledge, and by utilizing tutoring dialogues that automatically assess user input. This paper will discuss the results of an experimental evaluation of the VCAEST environment compared to an expert-led live-action training simulation.

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