Skip to content

Instantly share code, notes, and snippets.

@kenwebb

kenwebb/xholonWorkbook.xml

Last active September 10, 2024 19:42
Show Gist options
  • Save kenwebb/9b07f80f7989de85b2c5987f3e6a0c73 to your computer and use it in GitHub Desktop.
Save kenwebb/9b07f80f7989de85b2c5987f3e6a0c73 to your computer and use it in GitHub Desktop.
Download ZIP
Homeostasis
Raw
xholonWorkbook.xml
<?xml version="1.0" encoding="UTF-8"?>
<!--Xholon Workbook http://www.primordion.com/Xholon/gwt/ MIT License, Copyright (C) Ken Webb, Tue Sep 10 2024 15:41:20 GMT-0400 (Eastern Daylight Time)-->
<XholonWorkbook>
<Notes><![CDATA[
Xholon
------
Title: Homeostasis
Description:
Url: http://www.primordion.com/Xholon/gwt/
InternalName: 9b07f80f7989de85b2c5987f3e6a0c73
Keywords:
My Notes
--------
4 September 2024
### References
() https://www.google.com/search?client=ubuntu-sn&channel=fs&q=model+of+homeostasis
() https://en.wikipedia.org/wiki/Homeostasis
All homeostatic control mechanisms have at least three interdependent components for the variable being regulated: a receptor, a control center, and an effector.
The receptor is the sensing component that monitors and responds to changes in the environment, either external or internal.
Receptors include thermoreceptors and mechanoreceptors.
Control centers include the respiratory center and the renin-angiotensin system.
An effector is the target acted on, to bring about the change back to the normal state.
At the cellular level, effectors include nuclear receptors that bring about changes in gene expression through up-regulation or down-regulation and act in negative feedback mechanisms.
An example of this is in the control of bile acids in the liver.
() https://www.britannica.com/science/homeostasis
homeostasis, any self-regulating process by which biological systems tend to maintain stability while adjusting to conditions that are optimal for survival.
If homeostasis is successful, life continues; if unsuccessful, disaster or death ensues. The stability attained is actually a dynamic equilibrium,
in which continuous change occurs yet relatively uniform conditions prevail.
() https://durhamcollege.ca/mydc/wp-content/uploads/sites/8/Homeostasis.pdf
Parts of a Homeostatic System
How does the body maintain this balance? No matter what variable is being maintained, all
homeostatic systems have the same three general component parts:
a receptor,
control centre, and
an effector
Example 1. A Non-Biological Example: Temperature regulation of a building.
One example of a homeostatic system is the temperature regulation of a building
(Figure 1). Note, that even within this system there are the three components of a
homeostatic system: a receptor, control centre, and an effector. The receptor is a
sensor in the thermostat that receives information about the temperature of the
building; the control centre is the computer found within the thermostat; and the
effector is the furnace itself.
The image shows two loops to the homeostatic system, this is used to indicate the
two outcomes of the control centre's comparison of the receptor information to the set
point, the desired temperature, either to heat the room or cool the room.
() https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4669363/?report=printable
A physiologist's view of homeostasis, Harold Modell et al, Adv Physiol Educ. 2015 Dec; 39(4): 259–266
Homeostasis is a core concept necessary for understanding the many regulatory mechanisms in physiology.
In this article, we present a standard model for homeostatic mechanisms to be used at the undergraduate level.
we propose a simplified model and vocabulary set for helping undergraduate students build effective mental models
of homeostatic regulation in physiological systems.
() https://www.teachengineering.org/activities/view/nyu_homeostasis_activity1
Hands-on Activity
Using Microcontrollers to Model Homeostasis
Students learn about homeostasis and create models by constructing simple feedback systems using Arduino boards, temperature sensors, LEDs and Arduino code.
Starting with pre-written code, students instruct LEDs to activate in response to the sensor detecting a certain temperature range.
They determine appropriate temperature ranges and alter the code accordingly.
When the temperature range is exceeded, a fan is engaged in order to achieve a cooling effect.
In this way, the principle of homeostasis is demonstrated.
To conclude, students write summary paragraphs relating their models to biological homeostasis.
includes Materials List, various guides
() https://www.ncbi.nlm.nih.gov/books/NBK559138/
Physiology, Homeostasis, Sabrina Libretti; Yana Puckett, Last Update: May 1, 2023.
() https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7076167/
) https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7076167/pdf/fphys-11-00200.pdf
Homeostasis: The Underappreciated and Far Too Often Ignored Central Organizing Principle of Physiology
George E. Billman
Front Physiol. 2020; 11: 200, Published online 2020 Mar 10. doi: 10.3389/fphys.2020.00200
Homeostasis has become the central unifying concept of physiology and
is defined as a self-regulating process by which an organism can maintain internal
stability while adjusting to changing external conditions
() https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11338816/
The contribution of granger causality analysis to our understanding of cardiovascular homeostasis:
from cardiovascular and respiratory interactions to central autonomic network control
Vincent Pichot, et al
Front Netw Physiol. 2024; 4: 1315316, Published online 2024 Aug 8. doi: 10.3389/fnetp.2024.1315316
Homeostatic regulation plays a fundamental role in maintenance of multicellular life.
... mathematical approaches such as Granger causality (GC) ...
In this review, the aim is to introduce GC and provide an overview of the key GC tools available.
the analysis of causal relationships within dynamic systems has become more and more used in the physiological field and
seems to be suited to capture complex interactions between time series such as RRI, SBP and RE,
allowing the detection and quantification of the strength and direction of couplings.
The most studied and promising approach is based on the notion of Granger causality (GC),
implying that if one time series has a causal influence on a second time series,
then the knowledge of the past of the first time series is useful to predict future values of the second time series
() https://en.wikipedia.org/wiki/Granger_causality
The Granger causality test is a statistical hypothesis test for determining whether one time series is useful in forecasting another, first proposed in 1969.
Rather than testing whether X causes Y, the Granger causality tests whether X forecasts Y.
A time series X is said to Granger-cause Y if it can be shown,
usually through a series of t-tests and F-tests on lagged values of X (and with lagged values of Y also included),
that those X values provide statistically significant information about future values of Y.
Software packages have been developed for measuring "Granger causality" in Python and R
() my "Time Series Forecasting in Python" book has a few pages on Granger Causality
() https://newteditor.org/software.html
Welcome to Newt Pathway Viewer & Editor
Newt is a free, web based, open source viewer and editor for pathways in Systems Biological Graphical Notation (SBGN),
Systems Biology Markup Language (SBML), and Simple Interaction Format (SIF).
It was written with a series of libraries and extensions based on Cytoscape.js with utmost customization in mind.
() http://www.pathwaycommons.org/pc2/formats#sif_relations
() https://www.annualreviews.org/content/journals/10.1146/annurev.physiol.68.033104.152158
LXRS AND FXR: The Yin and Yang of Cholesterol and Fat Metabolism
Nada Y. Kalaany, et al
Liver X receptors (LXRs) and farnesoid X receptor (FXR) are nuclear receptors that function
as intracellular sensors for sterols and bile acids, respectively.
In response to their ligands, these receptors induce transcriptional responses that maintain
a balanced, finely tuned regulation of cholesterol and bile acid metabolism.
LXRs also permit the efficient storage of carbohydrate- and fat-derived energy, whereas FXR activation results
in an overall decrease in triglyceride levels and modulation of glucose metabolism.
The elegant, dual interplay between these two receptor systems suggests that they coevolved to constitute
a highly sensitive and efficient system for the maintenance of total body fat and cholesterol homeostasis.
Emerging evidence suggests that the tissue-specific action of these receptors is also crucial for the proper function
of the cardiovascular, immune, reproductive, endocrine pancreas, renal, and central nervous systems.
Together, LXRs and FXR represent potential therapeutic targets for the treatment and prevention of numerous metabolic and lipid-related diseases.
() https://bio.libretexts.org/Courses/Lumen_Learning/Anatomy_and_Physiology_I_(Lumen)/04%3A_Module_2-_Homeostasis
4: Module 2- Homeostasis
- 8 sections
() https://openstax.org/details/books/anatomy-and-physiology-2e
downloadable book PDF - I downloaded it - it treats homeostasis as a basic concept that comes up in every chapter
Anatomy and Physiology 2e is developed to meet the scope and sequence for a two-semester human anatomy and physiology course for life science and allied health majors.
The book is organized by body systems.
The revision focuses on inclusive and equitable instruction and includes new student support.
Illustrations have been extensively revised to be clearer and more inclusive.
The web-based version of Anatomy and Physiology 2e also features links to surgical videos, histology, and interactive diagrams.
Please learn more about the changes by previewing the preface.
() https://openstax.org/books/anatomy-and-physiology-2e/pages/1-5-homeostasis
I downloaded this chapter separately
() https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7460395/
A System Dynamics Simulation Applied to Healthcare: A Systematic Review
Mohammad Reza Davahli, et al
Int J Environ Res Public Health. 2020 Aug; 17(16): 5741., Published online 2020 Aug 8. doi: 10.3390/ijerph17165741
- has a large number of references
- "homeostasis" only occurs twice in the paper
Abstract
In recent years, there has been significant interest in developing system dynamics simulation models to analyze complex healthcare problems.
However, there is a lack of studies seeking to summarize the available papers in healthcare and present evidence on the effectiveness of system dynamics simulation in this area.
The present paper draws on a systematic selection of published literature from 2000 to 2019,
in order to form a comprehensive view of current applications of system dynamics methodology that address complex healthcare issues.
The results indicate that the application of system dynamics has attracted significant attention from healthcare researchers since 2013.
To date, articles on system dynamics have focused on a variety of healthcare topics.
The most popular research areas among the reviewed papers included the topics of patient flow, obesity, workforce demand, and HIV/AIDS.
Finally, the quality of the included papers was assessed based on a proposed ranking system, and ways to improve the system dynamics models’ quality were discussed.
5.1. Patient Flow
The most popular research area among the reviewed papers is patient flow.
() https://scholar.google.com/scholar_lookup?journal=J.+Oper.+Res.+Soc.&title=An+evaluation+of+the+applicability+of+system+dynamics+to+patient+flow+modelling
** research at Ottawa Hospital 2010 **
An evaluation of the applicability of system dynamics to patient flow modelling
S Vanderby, MW Carter
Journal of the Operational Research Society, 2010, Taylor & Francis
Abstract
The objective of this research is to determine whether Systems Dynamics (SD) is a beneficial method for modelling hospital patient flow from a strategic planning perspective.
While discrete event simulation has frequently been used as a tool for analysing and improving patient flow in health care settings,
the desire to assess and understand patient flow and resource demand from a more strategic, and therefore aggregate, perspective led to the use of SD.
To evaluate the suitability of such an approach, a model was developed in collaboration with the General Campus at The Ottawa Hospital
with particular attention paid to the delays experienced by patients in the emergency department.
The modelling techniques used, model validation and scenarios tested with the model are discussed, accompanied by comments regarding the appropriateness of SD for such a model.
() https://www.tandfonline.com/doi/abs/10.1057/jors.2009.150
An evaluation of the applicability of system dynamics to patient flow modelling
S Vanderby & M W Carter
cited 29 times, all citing papers are listed here
() https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7099916/
J Oper Res Soc. 2011; 62(8): 1431–1451, Published online 2010 Oct 13. doi: 10.1057/jors.2010.20
Applications of simulation within the healthcare context
K Katsaliaki corresponding author, and N Mustafee
cited 32 times
A large number of studies have applied simulation to a multitude of issues relating to healthcare.
These studies have been published in a number of unrelated publishing outlets, which may hamper the widespread reference and use of such resources.
In this paper, we analyse existing research in healthcare simulation in order to categorise and synthesise it in a meaningful manner.
The simulation modelling techniques that were found appropriate for the purposes of this study are
Monte Carlo Simulation (MCS),
Discrete-Event Simulation (DES),
System Dynamics (SD) and
Agent-Based Simulation (ABS).
Those who wish to have an introduction to the aforementioned techniques can refer to
Rubinstein (1981) for MCS,
Robinson (1994) for DES, and
Sterman (2001) for SD.
ABS is the most recent of the four simulation methods used since the mid-1990s. A brief description of ABS is provided below.
ABS is a computational technique for modelling the actions and interactions of autonomous individuals (agents) in a network.
The objective here is to assess the effects of these agents on the system as a whole (and ‘not to’ assess the effect of individual agents on the system).
ABS is particularly appealing for modelling scenarios in which the consequences on the collective level are not obvious
even when the assumptions on the individual level are very simple.
This is so because ABS has the capability of generating complex properties emerging from the network of interactions among the agents,
although the in-built rules of the individual agents’ behaviour are quite simple.
Of the selected papers, MCS seems by far to be (69%) the most applied method dealing with health issues.
It is followed by DES and SD.
Finally, the method with the least number of papers is ABS—this is not a surprise since it is the most recently developed simulation technique.
() https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10829493/
Agent-based social simulations for health crises response: utilising the everyday digital health perspective
Jason Tucker corresponding author, and Fabian Lorig
Front Public Health. 2023; 11: 1337151, Published online 2024 Jan 17. doi: 10.3389/fpubh.2023.1337151
- I downloaded this paper
During the COVID-19 pandemic, there was a momentous increase in interest in how artificial intelligence (AI) systems could be used to manage the crisis
Within this context, agent-based social simulations (ABSS) proved to be very well suited to informing policy decision making.
ABSS aim to model and simulate the actions and interactions of intelligent agents,
creating virtual populations or social systems composed of autonomous (artificial) individuals.
These virtual societies can serve as a “testbed” for investigating and comparing different policy interventions and scenarios prior to their implementation.
While ABSS cannot predict the future, it is a powerful tool to help inform policy makers of potential outcomes or consequences of interventions.
ABSS enables decision makers to “play” with policy, and variations of policies, and investigate their impact under different circumstances and scenarios.
Doing so in a virtual population allows for the conducting of experiments in a time and cost-efficient manner,
removes the risk of harming real-world individuals and can facilitate greater levels of preparedness and response to health crises.
A simulation paradigm that is particularly well suited to simulate individual behaviour is ABSS. ABSS make use of an artificial population of autonomous individuals, so called Agents, each of which is characterised by a set of attributes, e.g., age, gender, and health status. Based on these personal attributes, its environment, and its individual needs and goals, each agent individually plans its actions by imitating human-like behaviour using AI (10). The goal of ABSS is to imitate the relevant aspects of the real-world population, i.e., composition and behaviour, as closely as possible to allow for drawing sound conclusions regarding the target system.
Traditional simulation approaches in healthcare and epidemiology aim to directly model the dynamics of the phenomenon of interest. In ABSS, however, the system’s behaviour and the corresponding macro-scale phenomena emerge from micro-scale agent behaviour. This allows not only for analysing what potential consequences a given scenario or intervention might result in but also provides a better understanding why certain effects can be observed. Gilbert and Troitzsch (21, p. 1) argue that individual-based simulations imply a “new way of thinking about social and economic processes,” due to the emergence of complex behaviour from simple actions and interactions. ABSS can be applied to review theories, to verify assumptions, and to generate data and can therefore, according to the authors, be considered a new method of theory development.
() https://en.wikipedia.org/wiki/Agent-based_social_simulation
ABSS
Agent-based social simulation (or ABSS)[1][2] consists of social simulations that are based on agent-based modeling, and implemented using artificial agent technologies. Agent-based social simulation is a scientific discipline concerned with simulation of social phenomena, using computer-based multiagent models. In these simulations, persons or group of persons are represented by agents. MABSS is a combination of social science, multiagent simulation and computer simulation.
ABSS models the different elements of the social systems using artificial agents, (varying on scale) and placing them in a computer simulated society to observe the behaviors of the agents. From this data it is possible to learn about the reactions of the artificial agents and translate them into the results of non-artificial agents and simulations. Three main fields in ABSS are agent-based computing, social science, and computer simulation.
Agent-based computing is the design of the model and agents, while the computer simulation is the part of the simulation of the agents in the model and the outcomes. The social science is a mixture of sciences and social part of the model. It is where social phenomena are developed and theorized. The main purpose of ABSS is to provide models and tools for agent-based simulation of social phenomena. With ABSS, one can explore different outcomes of phenomena where it may not be possible to view the outcome in real life. It can provide us valuable information on society and the outcomes of social events or phenomena.
() https://www.jasss.org/index_by_issue.html
JASSS is an interdisciplinary journal for the exploration and understanding of social processes by means of computer simulation
- numerous articles re Agent-based model
The Journal of Artificial Societies and Social Simulation is an interdisciplinary journal for the exploration and understanding of social processes by means of computer simulation. Since its first issue in 1998, it has been a world-wide leading reference for readers interested in social simulation and the application of computer simulation in the social sciences.
() https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9140672/
Modelling Granular Process Flow Information to Reduce Bottlenecks in the Emergency Department
Marian Amissah and Sudakshina Lahiri
Healthcare (Basel). 2022 May; 10(5): 942, Published online 2022 May 19. doi: 10.3390/healthcare10050942
Increasing demand and changing case-mix have resulted in bottlenecks and longer waiting times in emergency departments (ED).
However, many process improvement efforts addressing the bottlenecks have limitations,
as they lack accurate models of the real system as input accounting for operational complexities.
To understand the limitations, this research modelled granular procedural information, to analyse processes
in a Level-1 ED of a 1200-bed teaching hospital in the UK.
Semi-structured interviews with 21 clinicians and direct observations provided the necessary information.
Results identified Majors as the most crowded area, hence, a systems modelling technique, role activity diagram,
was used to derive highly granular process maps illustrating care in Majors which were further validated by 6 additional clinicians.
Bottlenecks observed in Majors included awaiting specialist input, tests outside the ED, awaiting transportation, bed search, and inpatient handover.
Process mapping revealed opportunities for using precedence information to reduce repeat tests; informed alerting;
and provisioning for operational complexity into ED processes as steps to potentially alleviate bottlenecks.
Another result is that this is the first study to map care processes in Majors,
the area within the ED that treats complex patients whose care journeys are susceptible to variations.
Findings have implications on the development of improvement approaches for managing bottlenecks.
In terms of modelling process flow, emergency medicine experts recommend applying system modelling techniques. Widely used in manufacturing, industrial engineering, and complex services [72,73,74], system modelling techniques are increasingly used in healthcare settings. Common among these are dataflow diagrams [75,76,77], flowcharting [76,78,79], Value Stream Mapping (VSM) [25,59,80,81,82], and Role Activity Diagram (RAD) [75,83,84,85]. Table 1 provides a comparison of the methods
An iterative process was taken to develop the RADs. First, using a content analysis approach, all terms relevant to the RADs were systematically extracted from the transcribed scripts including roles, activities, interactions between roles and units, resources, and decision questions along the patient flow. The extracted terms were then used as input into Microsoft Visio to construct the RADs. The specific steps to derive the RADs can be found in earlier publications [75,76]. The resulting RADs generated detailed process maps comprised of activities that were carried out by staff in the department. Process maps are commonly used tools in business and industry to support the understanding of complex systems for quality improvement [92]. Next, a two-step procedure was followed to verify and validate the RAD-based process maps.
() search: role activity diagram (RAD)
- there is a lot of material available
() https://scitech.bournemouth.ac.uk/staff/kphalp/bpr2.pdf
Role Activity Diagrams (RADs)
- has a focus on Interactions
- somehow related to Petri Nets ?
() https://www.researchgate.net/publication/262943544_Improved_Workflow_Modelling_using_Role_Activity_Diagram-based_modelling_with_Applications_to_a_Radiology_Service_Case_Study
) file:///home/ken/Downloads/Nagesh_SHUKLA_ComputerMethodsandProgramsinBiomedicine.pdf
Improved Workflow Modelling using Role Activity Diagram -based Modelling with Application to a Radiology Service Case Study
Nagesh Shukla1,2, John E. Keast1, Darek Ceglarek
Article in Computer Methods and Programs in Biomedicine · June 2014
() also these files that I downloaded to ~/Dowmloads
Systems-In-Focus_-Energy.stmx
fpubh-09-652694.pdf
'Data Sheet 1.docx'
P1393.pdf
SD_ABM.pdf
12874_2024_Article_2252.pdf
P1209.pdf
Diabetes_SystemISDC04.pdf
0960488.pdf
Nagesh_SHUKLA_ComputerMethodsandProgramsinBiomedicine.pdf
fpubh-11-1337151.pdf
41274_2011_Article_BFjors201020.pdf
Anatomy_and_Physiology_2e_-_WEB_c9nD9QL.pdf
HomeostasisFull.pdf
"A physiologist's view of homeostasis - PMC.pdf"
### 10 Sept 2024, youtube
() https://www.youtube.com
search: homeostasis
- I watched the following 3 excellent videos
() https://www.youtube.com/watch?v=Iz0Q9nTZCw4
Homeostasis and Negative/Positive Feedback, Amoeba Sisters
- no overall vocabulary I can use
() https://www.youtube.com/watch?v=CMEre2VdSSc
Homeostasis, Dr Matt & Dr Mike
Negative Feedback - negates the stimulus
+ *************** Environment **************************************** +
+
1. Stimulus - change in the environment (ex: temperature goes up)
+ ========= Organism ================================== +
+
2. Receptor, Sensor - picks up the change, and
sends afferent signal to
3. Control Center - decides what to do, and
sends efferent signal to
4. Effector - (ex: sweat glands, cells, muscles)
+
+ ========= Organism ================================== +
+
+ *************** Environment **************************************** +
Positive Feedback - reinforces the stimulus
() https://www.youtube.com/watch?v=0aGMiYVRg_E
Homeostasis, Ninja Nerd
- ???
- the examples are more specific with detailed physiology diagrams (ex: glucose, insulin, pancreas)
- stimulus (high glucose)
- afferent signals to pancreas
- GLUT resceptors on pancreas
- effector
- response
- OR stimulus (low glucose, pancreas, glucagon, liver
]]></Notes>
<_-.XholonClass>
<HomeostaticSystem/>
<Homeostasis/>
<Feedback>
<NegativeFeedback/>
<PositiveFeedback/>
</Feedback>
<!-- Parts of a Homeostatic System -->
<Stimulus/>
<Receptor/> <!-- or Sensor -->
<AfferentSignal/>
<ControlCenter/>
<EfferentSignal/>
<Effector/>
<Response/>
</_-.XholonClass>
<xholonClassDetails>
<!--<Block>
<port name="height" connector="Height"/>
</Block>-->
</xholonClassDetails>
<HomeostaticSystem>
<Receptor/>
<ControlCenter/>
<Effector/>
</HomeostaticSystem>
<Blockbehavior implName="org.primordion.xholon.base.Behavior_gwtjs"><![CDATA[
var a = 123;
var b = 456;
var c = a * b;
if (console) {
console.log(c);
}
//# sourceURL=Blockbehavior.js
]]></Blockbehavior>
<Heightbehavior implName="org.primordion.xholon.base.Behavior_gwtjs"><![CDATA[
var myHeight, testing;
var beh = {
postConfigure: function() {
testing = Math.floor(Math.random() * 10);
myHeight = this.cnode.parent();
},
act: function() {
myHeight.println(this.toString());
},
toString: function() {
return "testing:" + testing;
}
}
//# sourceURL=Heightbehavior.js
]]></Heightbehavior>
<Brickbehavior implName="org.primordion.xholon.base.Behavior_gwtjs"><![CDATA[
$wnd.xh.Brickbehavior = function Brickbehavior() {}
$wnd.xh.Brickbehavior.prototype.postConfigure = function() {
this.brick = this.cnode.parent();
this.iam = " red brick";
};
$wnd.xh.Brickbehavior.prototype.act = function() {
this.brick.println("I am a" + this.iam);
};
//# sourceURL=Brickbehavior.js
]]></Brickbehavior>
<Brickbehavior implName="org.primordion.xholon.base.Behavior_gwtjs"><![CDATA[
console.log("I'm another brick behavior");
]]></Brickbehavior>
<SvgClient><Attribute_String roleName="svgUri"><![CDATA[data:image/svg+xml,
<svg width="100" height="50" xmlns="http://www.w3.org/2000/svg">
<g>
<title>Receptor</title>
<rect id="HomeostaticSystem/Receptor" fill="#98FB98" height="50" width="50" x="25" y="0"/>
<g>
<title>Height</title>
<rect id="HomeostaticSystem/Receptor" fill="#6AB06A" height="50" width="10" x="80" y="0"/>
</g>
</g>
</svg>
]]></Attribute_String><Attribute_String roleName="setup">${MODELNAME_DEFAULT},${SVGURI_DEFAULT}</Attribute_String></SvgClient>
</XholonWorkbook>
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment