Visualization - Science topic

I agree with Manuele. I can add that for proteins produced in bacteria, a C-term tag allows to purify only the fully translated products. For large proteins this can be important since stop of translation is a common problem (especially if the DNA has not been optimized for the codon usage in coli). Saying that if a small proportion of the protein is fully translated, the purification will be proportional. As Manuele said, in other host the N-terminal (or the C-terminal) can be modified or important for secretion, therefore the position of the tag should be considered accordingly.

Double tags allow to use sequential purification of the protein that will increase the purity of the protein. In my hands, 6 (or 10) His tag works fine (i.e. purification is good) if the protein is well expressed (and soluble). If protein expression level is low then more contaminants are co-purified. In this case a double tag may help.

Hope this is helpful. Best.

M

One can image RNA, DNA, with TEM, but unless you have a means to label the hybrid uniquely, with an electron dense label, I do not believe it will be unique from other DNA/RNA, as seen with the TEM.

I very intersted to collabrate this group.

Yes

İlayda NUR Belen

Please, look at discussion in https://www.researchgate.net/post/MRNA_synthesis_and_purification_T7_MEGAscript_Transcription_Kit_troubleshooting

There is one advice by @Victor G Stepanov that I would recommend too: "Actually, it might be useful to set up the T7 transcription reaction in small scale (~ 20-30 mcl), and then resolve the reaction mix on a gel to see if you had the mRNA synthesized in the first place."

For Fatigue Analysis I would rather recommend to use Fe-Safe (an specialized Application for Fracture and Fatigue which comes in bundle of Simulia Abaqus software itself).

Say you want to color just 2 objects in VMD different colors, it is relatively simple. You can do it in two main ways.

1) The easiest is to go to the VMD main window, to the "Graphics" tab, and then click on the "Representations..." tab.

A window will appear. In this window, you should select the coloring method ColorID from the drop-down menu. The drawing method and other options are down to you.

Now you need to make selections of the proteins. Go into the GRO or PDB file and write down the atom number with which each protein starts and ends. Into the "Graphical Representations" window that previously opened write those numbers into the "Selected Atoms" line like this: index (first atom) to (last atom) and then press enter.

For example: index 0 to 273

Next, you are going to click on the "Create Rep" button in the same window, I should create a new representation by copying the previous one. Change it by writing the atom numbers of the second protein (example: index 274 to 547) and press enter.

Afterward, you just need to change the color ID number by choosing from the drop-down menu the color you like for each protein.

In summary: Graphics -> Representations...-> select "index (first atom) to (last atom) -> ColorID coloring method -> repeat process after creating new representation just with the second proteins atom numbers

2) The more advanced method is to go to "Extensions" in VMD Main -> Tk Console and then paste this into the command prompt and press enter:

mol delrep 0 top

#First protein

mol selection {index 0 to 273}

mol color ColorID 0

mol representation Lines

mol addrep top

# Second protein

mol selection {index 274 to 547}

mol color ColorID 1

mol representation Lines

mol addrep top

Just change the code by adding your atom ID numbers, ColorID numbers, and drawing methods. You can copy-paste the section of code that has "mol selection" to "mol addrep top" for any number of proteins you want to color. I even used Excel to create code to color 200 peptides with different colors automatically.

NOTE: Be careful when selecting atom indices in different files. GRO files count the first atom from 0, PDB files from 1.

Hedgs G is pretty similar to Cohen's d; in fact, it is kind of like a correction for Cohen's d (see page 4: https://pubs.asha.org/doi/pdf/10.1044/cicsd_33_S_42#:~:text=This%20means%20that%20if%20repeated,a%20high%20of%201.42%20SD.)

Therefore, I'm pretty sure that a negative value interpretation is the same as with Cohen's d: i.e., negative usually means the control group had a higher mean than the treatment group. I think a lot of studies tend to use the absolute value of Cohen's d and then you look at the averages to see which is bigger, which might be what is confusing you.

If a study only reports a median, I don't know if you can calculate an effect size that requires an average from it. In some cases, it is quite impossilbe if the variance should be pooled (for many dependent samples cases). You might try emailing the authors and seeing if they're willing to either share the data or provide the average for you?

Do you have a pdb ID for the protein you wish to display?

To match both the lighting and the matte finish of the surfaces, fine-tuning of various lighting and reflectivity settings is needed. This is most easely done using the Lighting setting Plugin: https://github.com/schrodinger/pymol-open-source/blob/master/data/startup/lightingsettings_gui/main.py, which allows you to control each of the relevant settings through a slider

Eldrin a Ca, at the end of this playlist (link: https://www.youtube.com/playlist?list=PLJnI4AlLzRgzn7jXdqz8vp12k--9eDbx4) i discuss how to interpret various kind of results in ABAQUS with outputs as per the ABAQUS documentation. PLease watch to learn how to interpret results. You can ask me on my WhatsApp as well: Https://wa.me/+923440907874.

Hello Junhao Rong, I've given some serious thought to a schlieren setup closer to that of a microscope. However, a typical microscopy lens, such as the ones used to measure droplet sizes in sprays, is not capable of producing schlieren images. With such a lens, we can only obtain shadow images, which I believe lack the sensitivity required to visualize vortices. I was considering that a potential solution might be to use a lens with a long focal length on the camera. Thank you for the discussion.

The cheapest fluorescent dye is fluorescein. It is highly water soluble and very bright. Its main disadvantage is poor photostability, meaning that it photobleaches quickly when exposed to light. Pyranine is another relatively inexpensive, bright fluorescent dye that is highly water soluble and probably more photostable than fluorescein.

Hi Avinash Shivdas . I guess you're looking for something like an R package, and probably there is, I'm not sure. However, a graph like your example, simple and abstract, can be easily done in any drawing software, like the ones used y graphic design (e.g. CorelDraw, Adobe Painter) and I would suggest Inkscape (https://inkscape.org/) which is open source software available for Linux, Windows and macOS. Learning to use it is pretty fast thanks to the lots of videos, tutorials and forums available in the Web, and you can easily use built-in tools to neatly align shapes and get a fancy and professional-looking result.

Hope this helps!

Dear Mr. Nambiar!

You raised an important point - how to teach science visually simply to children. The solution here is a case - and context-dependent one: what kind of subject you teach (math, psychology, biology, etc.) and the culture of the children they are educated along with (parents, family relations, school community):

1) Wiebels K, Moreau D. Dynamic Data Visualizations to Enhance Insight and Communication Across the Life Cycle of a Scientific Project. Advances in Methods and Practices in Psychological Science. 2023;6(3). doi:10.1177/25152459231160103, Open access:

2) Knox, J., Kontorovich, I. Growing research groves to visualize young students’ learning in small groups. Math Ed Res J 35, 401–425 (2023). https://doi.org/10.1007/s13394-022-00422-0, Open access:

Yours sincerely, Bulcsu Szekely

Safiul Haque Chowdhury

Thank you very much

Cheng Zeng I can share my thoughts and experiences to help you and your team in this journey. To make it accessible and engaging, let's break down the key aspects of your proposed interface and discuss them one by one.

You can look into Online applets that present the evolution of the polarization vector associated with single Qubit quantum states on or inside of the Bloch sphere, under the effect of Unitary transformation or decoherence transformations.

On the Quantum Computing platform QISKIT, this can also be possibly visualized.

Thankyou so much Sir. But i resolved the issue as I uninstalled the security guard then it immediately got installed normally.

Thank you very much, Illia, for sending me the figure. I guess the frequency of the collocate is given via the saturation of the colour of the spot, whereas the distance from the node indicates the real distance of the collocate from the node in a text (your span is -5/+5).

Could your problem be with the color metric solution? Are you using HRP conjugated ABs? Is your colorization agent DAB? If so, check the age of it. They do go bad over time and it's best to store them in the freezer when not using them.

Qamar Ul Islam thank you!

Graphia (https://graphia.app/) is a tool for the visualisation and analysis of graphs in 3D. It was specifically designed to be able to display large and complex networks.

Rachel Chase The choice of ordination method for visualizing the effect of different levels of aridity on a set of 30 plant traits depends on several factors, including the nature of your data and your specific research goals. Here are a few commonly used ordination methods and considerations for your scenario:

1. Principal Component Analysis (PCA): PCA is a widely used ordination method that can help reduce the dimensionality of your dataset. It's effective for visualizing patterns and exploring relationships among multiple variables, making it suitable for assessing how aridity levels affect a set of plant traits. PCA condenses the information into a few principal components, making it easier to visualize patterns.

2. Redundancy Analysis (RDA): RDA is an extension of PCA that allows you to explore the relationship between your explanatory variable (aridity levels) and multiple response variables (plant traits). RDA explicitly incorporates the explanatory variable into the ordination, making it suitable for assessing how aridity influences the trait variation.

3. Canonical Correspondence Analysis (CCA): CCA is another multivariate technique that combines aspects of PCA and regression analysis. It's particularly useful when you have ecological data, such as species abundance or plant traits, and environmental variables like aridity levels. CCA can show how plant traits are associated with different aridity levels.

4. Non-metric Multidimensional Scaling (NMDS): NMDS is a robust ordination method that does not assume linear relationships between variables. It's helpful when you suspect non-linear responses to aridity levels. NMDS provides a visual representation of the similarity or dissimilarity in plant traits across aridity gradients.

5. Constrained Ordination (e.g., CCA, RDA): If you have strong hypotheses about how aridity affects specific plant traits, constrained ordination methods like CCA or RDA may be appropriate. These methods allow you to test the significance of aridity levels in explaining trait variation.

The "best" method depends on the characteristics of your dataset and research objectives. Consider the assumptions of each method, the nature of the relationship you expect between aridity and plant traits, and the interpretability of the results. It may also be valuable to perform multiple ordination methods and compare the results to gain a more comprehensive understanding of the data.

Yousef Bahrambeigi many thanks for your answer

Actually I'm using Openseespy in Python, you can transfer your code into python and just use the visual part of the function.

Use the Fisher criterion or the method of variance analysis (ANOVA)

Python is relatively more useful but R is also good

Dinesh Kumar Duraisamy

The antipodes of twisted fibrils cannot be determined using TEM and CEM. You will not distinguish between the left and right hands with your eyes if their morphology from above and below is the same. Also, you can't tell which antipode you see if you don't see another side by side. Alpha and beta glucose will look like white crystals under the microscope.

Hi Saif, thanks for your quick and elaborate response! However, what I meant was that I wanted to plot the Pearson's coefficient values in a meaningful way, NOT the transformed data since those numbers are just arbitrary values to obtain a normalised population. The Pearson's scale would range from 1 (full colocalisation/dependency) to 0 (no correlation) to -1 (mutual exclusivity), whereas the Fisher transformed values have no meaning. I think I will just plot the spread of all values without a mean, and manually add the mean, p-value etc. to keep the intuitive visual and provide all relevant parameters.

This is not a question, but some sort of advertising/publicity plug.

Delete/Ignore

Shaolin Lü,yes the dataset is open.I had analyzed the high-rated GPS data from SANT GPS station of Chile, with respect to the 57th, 58th, and 59th day (Julian days) of 2010, which was related to the great Chile Earthquake 2010.

You can use Google Classroom.

Each software has a specific way of reading the .pdb file...

Dear Merve Özcan ,

VESTA can be used in Windows, but please note that to visualize Optimised structure, Xcrysden should be used.

Please let me know, if further help is required.

--

Best wishes

Vipul

Thank you, Ahmed Khalid Ahmed Asinger, for the explanation.

After conducting your data analysis in SPSS for your thesis, there are several data visualization tools you can consider to present your findings effectively. Here are some popular options:

Consider the complexity of your data, the type of visualization you want to create, and your comfort level with different tools when selecting the most suitable option. Additionally, it's important to ensure that the chosen tool aligns with any specific guidelines or requirements set by your institution or intended publication platform.

Remember that the purpose of data visualization is to effectively communicate your findings, so choose tools and techniques that best represent and highlight the key insights from your analysis.

Hi Ruben,

I was also inspired by the research to add some clusters to my visualized lda model but I did not succeed although I tried different ways of visualizing by LDAvis or pyLDAvis.

I wonder if you found a way that could share with me?

Thank you!

Look for spines (ARC protein deposits) at locations where synchronous activation of neurons AND fine fibres of branched axons of other neurons are co-located, such as in the cerebral cortex.

I would not expect to see them where associative memory formation is not considered likely.

1. UCSF Chimera: UCSF Chimera is a powerful molecular visualization tool that is available for free download. It has a user-friendly interface and a wide range of features, including the ability to visualize and analyze protein structures.

2. Jmol: Jmol is a free, open-source molecular visualization tool that is designed for the display of molecular structures in 3D. It has a user-friendly interface and a range of features, including the ability to mark specific amino acids and customize the display of the protein.

3. Yasara: Yasara is a molecular modeling and simulation software that includes a user-friendly molecular visualization tool. It has a range of features, including the ability to mark specific amino acids and customize the display of the protein.

Pretty old thread, but for everyone how faces this problem:

As long as there is some sort of equivalent or similar element available from the ABAQUS library you can use a work around using these as dummy variables together with a UMAT.

More detailed explanation can be found here:

Hi, this is ChatGPT Answer, I have not tried but I hope it would work

To include self-citations only in VOSViewer or Biblioshiny, you can follow these steps:

Note that the exact steps may vary slightly depending on the version and settings of VOSViewer or Biblioshiny that you are using.

Abderrahmane Boudribila Thank you

I use wordart https://wordart.com/

Variations on this question have been asked here several times. The basic conclusion is that all of the major qualitative data analysis programs (ATLAST.ti, Dedoose, MAXQDA, and NVivo) do essentially the same thing, but there are meaningful differences in how they implement these procedures.

The best thing to do is examine the training materials that these programs make available at their websites to determine which one feels most comfortable to you.

Thank you for your answers, really appreciated.

I want to visualise multiple clinical variables ( temperature , blood pressure, sugar… ) for multiple patients, recorded in multiple clinical visits by time.

sure. Deleted.

Anelia Kurteva Thank you for your advice. Thanks?(?ω?)?

But I just want to visualize RDF triples, and I don't want to use neo4j to store RDF triples.

My data is stored in RDF Databases and queried by SPARQL Endpoint.

Thank you sir for the suggestion.

try this one:

all the best

fred

Hello everyone.

First, Heena, please have a look at my advisor's ResearchGate profile. His name is James Liburdy and you can find him via my RG profile. He used to do flow vis of air jets in a wind tunnel. Just go through his publications and they will give you a sense of how to get started with air flow vis.

Second, because this thread is about flow visualization, I would like to let everybody know that we've started a Flow visualization Stack exchange forum. We are building a community currently. Once we have 60 people we will be allowed to proceed to a private beta version of the forum. Please join us if you are interested in flow vis and have questions to ask. Here's the link: https://area51.stackexchange.com/proposals/127312/flow-visualization?referrer=NTJlZjIyYzI3Zjk4N2I1NDZmMTJhZDUxMTViODcwMWUyNTM4OTI1YTU1OTYxN2ZkNDcwY2U2ZWI5NmU2OTY5OGhTtnNa4jEjFBFgB4o_K-u-LTdCqWX8yd8vul6HHcUb0

Update: The company replied and said they will remove my publication from their references. We will see if they do it and if they will remove the other papers with trichloroethanol.

If you want to use Python tools, you could use a combination of BioPython (https://biopython.org/wiki/Phylo) for conversion from nexus to newick tree format and use ete3 (http://etetoolkit.org/) for visualization.

Approximately 50 particles in static condition.

I totally agree that (good) illustrations are essential to draw in those who are unfamiliar with the work, especially those who are less scientifically inclined.

Thank you. Subhankar Banerjee

Thank you.

Hi Syed, if you decide to contact them & they are interested, you can email me deborah.hilton@gmail.com maybe I can help with analysis in Excel or reviewing questionnaire, but they maybe too busy with their orders etc. Kind Regards.

The following link is also very useful: https://link.springer.com/article/10.1007/BF02537208#:~:text=A%20rapid%20unidimensional%20thin%2Dlayer,and%20cholesterol%20are%20thereby%20achieved.

You can research the business side of sports, if you are interested - optimizing training/preparation costs; maximizing events' revenue, ecx.

no sir

Thank you Yulia Panina, I did more tests and the results are better (no tail and green ring around the red droplet), thus it seems to be matter of sample preparation.

Probably if pH is too high, the FCF dye loses its property.

Many thanks for your interest.

Best wishes,

Filippo

Hi Somaya A.Amer. With an inverted microscope, you would have to invert your slide as well. If you have a coverslip covering your cells, this should face your objective lens. If your cells are fluorescent, you have to make sure your acquisition parameters are correct (excitation laser wavelength, emission range, gain settings, etc).

Dears;

I'm having the same issue. Did you manage to find a solution? Is it possible for you to share it with me?

Dear Ravi L. Hadimani ,

Look the link, maybe useful.

Regards,

Shafagat

hi.,

kindly refer this link:

Best wishes..

OK, if you decide to use chemcraft with the output that includes the keyword pop=full and gfinput, the surfaces are visualized as follows:

Tools > Orbitals > Render molecular orbitals > select the orbitals you want

and ready!

Nitin Sharma, Try this

library(rpart)

library(randomForest)

library(tidyr)

library(ggplot2)

set.seed(42)

r <- rpart(Species~., data = iris)

for(i in 1:10) {

f <- randomForest(Species~.,data = iris, ntree = i)

r_pred = predict(r, iris, type = 'class')}

f

errors <- data.frame(f[["err.rate"]])

errors$ntree <- seq(1,10, by = 1)

error_df <- gather(errors, "Species", "Value", 1:4)

ggplot(error_df, aes(x = Species, y = Value, color = as.factor(ntree)))+

geom_point()+

labs(col = "ntree")

1) Always set seeds when working with random functions, this allows you to reproduce numbers. If you run your code multiple times, you will get different results each time.

2) Error rates can be fetched from the rf-formula object (f) by extracting it with [["err.rate]]. Open the f-object in the viewer to see the other information you can extract.

3) The rest of the code will gather your data in a long format, which makes it easier to plot, and code to plot it as a scatterplot with colorcoding for each ntree-run. Code can be expanded on to show values etc.

Hope this helps

Best !!

AN

After setting your brightness and contrast click on the "set" button and then tick the first of the two check boxes "propagate to all other n channel images". This should apply the settings to the whole stack (but also to any other images that are open at the same time)

Dear Vrushali Lanjewar thank you so much.......

Can you say more about what you mean by qualitative analysis?

Samir el Shawish showed me the way.Here is his profile:

You can do it by creating a display group for each element set. When adding all of them, the feature edges will separate each element set.

Thanks!

Try

ShinyGO

webgestat

funrich

David

Dear Ana,

Check that if your model fully supports these outputs. Make sure that the analysis type, material properties, and element type are defined in a way to produce these outputs.

Dear Mrs. Matusiak

Virtual and augmented reality research in the architecture field reveals a wide range of potential applications for systems to assist designers, laypeople, and decision-makers in the architectural design process. There is an urgent need to reconsider how we use the set of collaborative technologies to move toward a more sustainable world. e a new vision of virtual reality (VR) as a discipline-agnostic platform for interdisciplinary integration of allied design, social, and environmental disciplines to address emerging challenges across the building sectors. This contribution is built in the following steps. First, we situate VR technologies within the evolving digital landscape and the underlying tensions in built-environment practices. Second, we characterize the challenges that have arisen as a result of using them to address challenges, illuminating our point with leading examples. Third, we conceptualize VR configurations and investigate underlying assumptions for their application across disciplinary scenarios. Fourth, we propose a vision of VR as a platform that can be used across disciplines.