Visual - Science topic

It depends on the substance being tested. For example, 10 microliters of 1 mg/ml of flavonoids can be seen clearly, but 10 mg/ml of terpenes that need to be colored can hardly be seen clearly.

You might try high resolution micro-x-ray tomography? It will produce A 3D image allowing 3D measurements, great detail.

In general, memos are part of the ongoing analysis process, rather than the results. The classic approach to reporting results in GT is to develop a set of conceptual categories and go through them one by one, with supportive quotations for each.

What specific changes in set design have digital technologies brought about?

Film is a medium that aims to create visual credibility. Scenes in films originate from imagination brought to life through technology, making them appear realistic. Thus, the term "realistic" refers to a lifelike quality that allows the audience to engage emotionally. Spectacular set designs naturally evoke and influence the emotions of viewers. Therefore, technology inevitably brings changes to set design, making it increasingly realistic. I hope this answer helps guide you in your research. If it does not fully align with your needs, I sincerely apologize.

Sigied Himawan Yudhanto you are most Welcomed

Your concern about the burden graphical abstracts place on researchers, who are often not professional designers, is also justified:

The question of whether graphical abstracts are "essential" depends on the nature of the study:

Instead of making graphical abstracts mandatory, journals and reviewers could adopt a more flexible approach:

In conclusion, while graphical abstracts have undeniable benefits, they should not overshadow the core scientific contribution of a study. A balanced approach that considers the needs and constraints of researchers is essential to maintaining fairness in academic publishing.

Dear Doctor

[Frameworks for measurement have been established and include public opinion polling and surveys, visual comparison, spatial metrics, and ethnographic work.

Visual pollution can manifest across levels of analysis, from micro instances that effect the individual to macro issues that impact society as a whole. Instances of visual pollution can take the form of plastic bags stuck in trees, advertisements with contrasting colors and content, which create an oversaturation of anthropogenic visual information within a landscape, to community-wide impacts of overcrowding, overhead power lines, or congestion. Poor urban planning and irregular built-up environments contrast with natural spaces, creating alienating landscapes. Using Pakistan as a case study, a detailed analysis of all visual pollution objects was published in 2022.]

Thank you, again. Winda Ariyani

yes i am

Dear Suddha Sourav thank you so much for your help!

I think I found the origin of the problem. As indicated here https://github.com/sccn/eeglab/blob/develop/functions/sigprocfunc/spectopo.m the function spectopo.m is used by default if the signal processing toolbox is installed and returns the power spectral density. Otherwise the function spec.m is used, the output of which is the power spectrum and not the PSD. The units are then different but the topography should be similar. See also https://github.com/sccn/eeglab/issues/172

Thanks again!

Dear professor Gammoudi,

Did you get any answer about the tool for Galois Lattice Visualization?

I'm looking too to a tool for Galois Lattice in my research work.

Best regards

Hi Ashly,

Have you found a way to do this?

Hello Lingyu,

I'm not sure if there is an optimal way to do it with roary_plots.py, since hundreds of genomes will still be many rows. Looking at the code of roary_plots.py, you should be able to increase the width of the tree to improve the clarity by changing the “colspan” parameter in the tree display. You can also improve the size and resolution of the image with the “figsize” and “dpi” parameters.

Best regards,

Adrian

nterference happens when two light waves meet and mix together. It is caused by two or more light waves coming together. Diffraction happens when a light wave bends around corners or through small openings. It is caused by light waves hitting an obstacle or passing through a small gap.

It seems that navigation doesn't really require locating the camera itself,

but real-time map-construction needs to know the camera's position.

To think more number of case studies regarding visual subcompetence in education.

Dear Titas De ,

CNNs can be trained and deployed more efficiently than VLMs, especially for large-scale datasets. This is due to their simpler architecture and the ability to leverage parallel processing hardware for faster computations.

https://www.picsellia.com/post/vlms-vs-cnns-a-new-era-dawning-in-computer-vision-performance#:~:text=CNNs%20can%20be%20trained%20and,processing%20hardware%20for%20faster%20computations.

Regards,

Shafagat

Practicality all engineering metallic alloys (except for a few special materials such as single crystalline superalloys) are in a polycrystalline state. This means that each crystal is defined in terms of its crystallographic orientation, its size and local topological environment and the properties of its grain boundaries. All such polycrystalline aggregates are subject to capillary driven competitive grain coarsening phenomena, provided that the temperature is high enough to overcome the activation barriers. In the metallurgical terminology these phenomena are often referred to as grain growth.

It is described as a process by which the mean grain size of an aggregate of crystals increases. The driving force for this results from the decrease in free energy which accompanies reduction in total grain boundary area. As the crystals become gradually larger, the curvature of the boundaries becomes smaller. This results in a tendency for larger grains to grow at the expense of smaller grains.

It should be emphasized that in many engineering applications grain growth is not desirable, as many mechanically beneficial properties of structural metallic alloys such as strength, strain-hardening and ductility are improved inversely to the average grain size.

Given a sufficiently high temperature and no microstructure features which impede grain boundary migration such as second phase particles or impurities that act on the grain boundaries, polycrystals will gradually evolve towards a single crystal. In real microstructures this goal is rarely achieved.

In two dimensions, a metastable state where coarsening ceases, is in principle possible, provided that all grain boundaries have exactly the same grain boundary energy and are connected with each other with 120° angles, so that the microstructure consisting then of equal hexagonally shaped crystals, is free of any grain-boundary curvature so that no capillary driven grain coarsening occurs.

It must be emphasized though that such a microstructure is only mechanically stable (and not in thermodynamic equilibrium) owing to the local mechanical equilibrium of the grain boundaries when abutting under 120°. In two dimensions such intersections are referred to as stable triple junctions.

From a thermodynamic viewpoint the driving force of grain coarsening is the overall reduction in the total grain boundary area per volume. Hence, even a microstructure that is under local mechanical equilibrium is not necessarily also in thermodynamic equilibrium.

In most cases, however, even in quasi two-dimensional situations as sometimes encountered in thin film grain structures, microstructures are not free of local curvatures of the grain boundaries involved. In three dimensions, curvature-free and at the same time compatible grain topologies are not possible in local mechanical equilibrium (MacPherson and Srolovitz, 2007). This means, that in three dimensions all polycrystalline microstructures must necessarily contain some local grain-boundary curvature.

A classical kinetic approach to describe such a gradual coarsening phenomenon can be derived by considering the velocity of a portion of grain boundary which is related to the pressure gradient across it as described above in terms of the phenomenological Turnbull rate expression for grain boundary motion.

This shows us that grains grow at a rate which is essentially proportional to the square root of time when considering that the initial grain size is much larger than the current average grain size .

It has to be emphasized at this point that in this derivation we have only considered the reduction in grain boundary energy for the driving pressure. Possible additional sources that might enter into the total energy reduction are that the grain boundary might be separating grains consisting of different phases, or one grain may have a higher dislocation density than another. If neither of these effects apply, the pressure can arise simply as a product of the curvature of the grain boundary. This pressure acts towards the centre of curvature.

It is interesting to note in that context that it is actually in a real microstructure not the average grain size that acts as a driving force for grain coarsening but it is the local curvature of the interface that matters. In other words, any local grain boundary curvature aims at reducing the total grain boundary area per volume by moving towards the center of the curvature radius. This elementary process of grain coarsening is explicitly guided by the local curvature and the grain does not 'know' if actual grain size.

The actual connection between grain size and local curvature comes simply through the principle that large grains have either very low curvatures, and hence, slow growth rates of their grain boundaries, or the curvature of their grain boundary facets points outwards, towards the surrounding smaller grains.

As mentioned above, a neutral, curvature free microstructure, irrespective of the actual grain size, occurs when all grains in a two-dimensional grain structure have the same hexagonal grain shape and mechanically stable intersecting triple junctions of 120°. A triple junction is the point at which three grain boundaries meet. This local mechanical equilibrium among three intersecting grain boundaries in two dimensions does only apply when all interfaces have the same grain boundary energy.

Mullins pointed out the random grain structures are, however, inherently unstable). In 2D, grain boundaries that are associated with microstructures that are characterized by certain size distribution actually have to ‘curve’ so that they can intersect at triple junctions under preservation of the local mechanical equilibrium. In two dimensions this means that grains with less than six sides have centers of curvature lying inside their boundaries. Conversely, grains with more than six sides have centers of curvature lying outside their boundaries. This gives rise to the driving pleasure derived above, being proportional to both the grain boundary energy and the local grain boundary curvature. The resulting velocity of boundary migration is always directed towards the centers of the interface curvature.

For two dimensional grain structures this observation was cast into the so-called N-6 rule. This rule states that an N-sided grain evolves under 2D grain growth. This so-called Mullins-von Neumann rule shows that actually the number of grain boundary sides determines the crystal growth and not the grain size itself. Topologically it should be noted, though, that larger grains are very unlikely to contain a small number of boundary facets, and so the two formulations are approximately equivalent.

Although in three dimensions this problem is more complicated the main kinetic and energetic characteristics of grain growth remain in principle unchanged, namely, (i) grain boundaries migrate towards their centers of their curvature; (ii) small grains have usually larger curvature values associated with their grain boundary facets and hence reveal higher grain boundary velocities so that these become smaller at the expense of larger grains; (iii) and the average grain size increases with time.

However, one main difference in grain growth exists between two dimensions and three dimensions. In three dimensions no grain topology exists (as in 2D), which satisfies both, zero curvature grain-boundary facets and local mechanical equilibrium at the grain boundary interaction lines and points. The application of 3D surface evolver simulations has recently shown that in average grain shrinkage occurs for grain shapes that have a smaller number of facet areas than 14. Grains that have a larger number of grain facets will in average become larger during grain growth. The minimal-area tessellating grain shape in three dimensions is the 14-sided tetrakaidecahedra, a shape whose surface is made up of six hexagons and eight squares.

I add some reads for more details.

Good Luck!

Thanks and sorry for the late reply!

I end up just collect/purify samples and use H NMR for that project. I would definitely try your solution for similar projects.

Detecting bacterial contamination visually on frozen chicken is challenging. Harmful bacteria, such as Salmonella, Campylobacter, and Escherichia coli, are not visible to the naked eye. Bacteria contamination often doesn't show any distinct physical signs, especially in frozen chicken. However, there are some indirect visual cues that can raise concerns: Discoloration: Healthy chicken meat should have a consistent pinkish color. If the meat shows grayish, greenish, or yellowish discoloration, it could be a sign of spoilage, which may be linked to bacterial growth. Ice Crystals or Frost: Excessive frost or large ice crystals on the chicken could indicate that the meat has been thawed and refrozen, a condition that might lead to bacterial growth during the thawing period. Slimy Texture: Although more of a tactile cue, if the chicken appears slimy, it’s often a sign of spoilage or bacterial growth, even though it's frozen. Unusual Odor (upon thawing): While the chicken is frozen, an off odor might not be detectable. However, if it smells rancid, sour, or like ammonia upon thawing, it’s a sign of contamination or spoilage. Damaged Packaging: Tears, holes, or leaks in the packaging can increase the risk of contamination. The packaging should be airtight and intact to prevent exposure to bacteria.

May consider using Permai fluorescence dye.

Hi Sergio,

I recommend Geneious Prime if you need more flexible software than CIIDER since you'll be working with smaller scales of DNA sequences. You can adjust and customise the settings to visualise the overlapping regions easily. Of course, other options like Snapgene, IGV, and ApE exist, but I'm not entirely familiar with those. All the best; I hope it helps!

1. No. The purpose of the heatmap is to visualize/explore patterns across different genes and conditions, not to present result of hypothesis tests.

2. No. But the larger the sample size, the easier it will be to distinguish random noise from robust patterns.

Natalia Kasian Brownian motion will mean that the particles will be moving violently in a liquid. However in a nematic LC, they could be more constrained to a plane, but I have no idea. Also these sizes are close to the resolution of SEM. I’d look to freeze-fracture or, better still, cryoEM for morphological studies of your system.

- Programmable lights can be used as a tool to guide robots in different environments. These lights range from software-controlled LEDs to more complex systems that display specific patterns or visual information to the robot.

- The accuracy of robots can be improved by incorporating smart lighting systems that provide clear signals or instructions. For example, lights can be used to mark paths or to alert the robot when it is approaching obstacles.

Dear Jhon W. González simple question, Do you think, we're made of mechanics atoms? I do not think I'm!

Did you ever read fine print of CERN or any others?

CERN is following standard modeling of atom. Yet after seven decades everything is in stage of theory of outcome. Nothing is for definite.

Up this date nothing inside of atom ( proton/neutron, electron/positron, quarks, Higgs) never been observed or proves of mechanical atoms phenomenon.

Sadly, our science connecting mechanical physics to nature that it is not working mechanically, unfortunately some one like you accepting one-dimension static mathematic for three-dimension of nature that it is changing constantly.

At end of day, I don't think you know anything about atom or nature of our universe.

prove it, you know!

Reading of last century ago, and accepting modeling, prediction, postulation is not science, yet you follow.

Thank you Fahad Alshehri

As per the LI-COR instruction manual for Li-8100A, negative flux values are valid measurements. You should not discard it from your analysis.

Good morning,

to determine the threshold value I know of several ways. The first is to determine a reference stream, with a known source. This can be done by taking a field measurement or visually interperpetrating a shaded relief. Then read the Flow Accumulation value in the neighbourhood of the designated reference source. The second method is the trial-and-error method, but to avoid wasting too much time I recommend automation in the R programming language. A script automatically tests different threshold values and generates vector files, at the end you should choose the one that is closest to reality. In general, it is best to go into the field, but when this is not possible, consider drone inspections, reviews of data bases such as Opeen Street Map or local information sources, atlases and cartographic studies.

If you have any questions, please feel free to contact me, I can help as an R package developer.

Best regards,

Jakub

Not a recommendation, but Biorbyt does carry a Rabbit pAb against Drosophila resilin: https://www.biorbyt.com/resilin-antibody-orb807146.html

Sharing the new TEDx talk on project Dream On India -

This may be due to strong interactional forces between the components of material. This also shows that the material is highly viscous and May be in gel form.

Thank you Dr. Woo.

I am trying the same unsucessfully. Follow

I think this is not possible always. However, teachers can follow mixed learning style to reach more students.

Uma lacuna é uma falta em uma superfície pintada. Pode ocorrer por diversos motivos que levaram à perda de uma ou mais partes de uma pintura: ressecamento do ambiente, quedas ou má intervenção

Basically the same as those used to examine any artwork: formalistic, iconographic and iconological analyses (for the last two, see Erwin Panofsky, Studies in Iconology, 1st published in 1939, and constantly reissued). But you might also want to study the way that Marshall McLuhan does so in his book, The Mechanical Bride, published in 1951, which specifically refers to newspaper texts, pictures and ads. Both these books lay the groundwork for much of Post-Modernist art analysis.

Kübik merkezli olduğu için bir tanesi merkez atom diğerleri onun etrafında kübik şekilde dizilir. (10 küpü +1).örgü atom modeli şeklinde dizilerek sürekli kübik yapı oluşturur. En son atom merkezi atom seçilebilir, bağımsız ve serbest hareket edebilir.

Yes, the same meaning: "rolling the window" = like = sliding the window.

Good luck Joseph Whittaker.

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.

Dear Naous Mohamed Have a look at https://www.researchgate.net/post/Best-graphics-program-for-making-scientific-illustrations-for-journal-articles for various good (and bad) suggestions.

Personally I used Biorender recently. It is a rather user-friendly tool (with limitations). You can try for free if it suits you. The only real drawback, it is not for free if you need publication quality pictures.

Best regards.

Thank you very much for the suggestions, I will try again. Best wishes. Malcolm Nobre Stefanie Meyer

Yes there are alternative ways to roughly identify anatomical locations without additional staining.

Here are two approaches that help you

1: Learn to identify key anatomical landmarks in the mouse brain. The brain atlases can be helpful resources. By comparing your sections to the atlas, you can get a rough idea of the anatomical location.

2: Another way is to use a bright-field microscope with a low magnification objective to get an overview of the entire brain slice. This may help you identify gross anatomical features.

Don L. F. Nilsen --- Don, It is true if you also assume there are distinct differences among our ideas. The other possibility "its all the same". In a way we form separations to study paradoxes. But without our desire to see separate parts the distinct differences disappear and we "see" the whole. - And, paradox (one of the academic "games" we love to play) is not apparent.

I think with the help of LigPlot+, you can see the 2D protein-protein interaction.

But in any case, you have to change the chain identifier of one of the proteins. you can manually edit the chain identifier directly in the PDB file using a text editor. Locate the lines that define the chain identifier and change the chain identifier accordingly. Save the modified PDB file after making the changes.

Dear Colleague Daniel Martinez,

Thanks! Your insights from the fields of ecology and our global work on natural resource conservation are very helpful for me to contextualize how this new tool of analysis can function responsibly. Thank you for taking the time to reply.

s. Jacob Schneider I assume that this question is related to the recent one (Do you recommend using a Disease Severity Index if you have digitally measured diseased area?). Transformation of leaf area percentages into a scale is possible, but you will lose a significant information content.

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).

May I know if yu managed to do energy minimisation for DNA?

Here are a few perspectives to consider:

- As AI systems become more capable of generating creative works like art, music, and writing, the notion of originality and authorship gets more complex. An AI system is trained on vast datasets and learns patterns and styles that it can remix into new creations. So even its most imaginative outputs are derived from human-created works. This leads some to question whether AI creations can ever be considered truly original.

- However, even for humans, complete originality is arguably rare - we are all influenced by and build upon the ideas, styles, and techniques of those before us. The creative process often involves remixing, iteratively improving, and finding new combinations, even when generating highly novel work. In this view, AI expands the creative palette, though attribution and intellectual property issues remain open questions.

- Visual collages assembled from multiple sources also have a long history of artistic use for creative expression and social commentary. The juxtaposition and recontextualization of disparate elements allow new insights and meanings to emerge from the composite. This may allegorically relate to emerging AI capabilities - synthetic media that remixed elements into a new "subject authorship" defined by the arrangement itself more than the original components.

- Some believe synthesizing creative works from abundantly available digital data simply accelerates the postmodern deconstruction of originality and sole authorship. It further dispels the notion of art and media requiring a human creator in an fixed historical context. From this perspective, evaluating AI outputs on originality alone misses their potential - the value may lie more in what effects they catalyze or perspectives they introduce.

There are good arguments on multiple sides, but the questions raised seem increasingly important as interactive AI becomes more ubiquitous. As creators, interpreters and consumers of synthetic media, an open and thoughtful discourse seems needed to guide both technology and policy. But I have only touched the surface of these complex issues from a non-expert view. There are likely many deeper dimensions left to consider for those immersed in visual arts and media studies. Please let me know if you would like me to expand or clarify any part of this response!

Artificial Intelligence (AI) has made remarkable advancements in recent years, but the idea of it being able to read minds seems far-fetched. However, if AI were capable of such a feat, it would undoubtedly revolutionize automated language translation. The impact would be immense and could potentially bridge the gap between different cultures and languages.

If AI could read minds, it would have a profound effect on automated language translation. Currently, translation algorithms rely on input text to generate accurate translations. However, this method often falls short in capturing the true meaning and nuances of a particular language. By being able to read minds, AI could understand the intent behind the words and phrases used by individuals.

This newfound ability would significantly enhance automated language translation by providing more context and accuracy. It would enable AI systems to comprehend idiomatic expressions, cultural references, and even sarcasm – elements that are often lost in current translation methods. As a result, communication between people speaking different languages would become more effective and seamless.

Moreover, if AI could read minds accurately, it could eliminate the need for any intermediary step in communication. People wouldn't have to type or speak their thoughts; instead, they could simply think them while AI translates them instantaneously. This direct mind-to-mind communication would break down barriers like never before and foster understanding among diverse groups of people.

However, despite these potential benefits, there are ethical concerns surrounding mind-reading technology that must be addressed. Privacy is one major issue that arises when discussing AI's ability to read minds. If an individual's thoughts can be accessed without consent or control over who can access them, it raises serious questions about personal autonomy and privacy rights.

In conclusion, while the idea of artificial intelligence reading minds may seem like science fiction at present time; if achieved successfully in the future - it will undoubtedly have a significant impact on automated language translation. The potential benefits include improved accuracy in translations as well as direct mind-to-mind communication. However, ethical considerations must be carefully addressed to ensure privacy and consent are respected.

Thank you sir

By utilizing SAR satellite products, one can assess the state of the ground surface prior to and following the eruption. Please refer to the following article for an illustrative example.

Here's a general process to do this:

Run SwissDock: Start by running your docking simulations in SwissDock to predict the binding of ligands to a protein target.

Export the Results: After the SwissDock simulations are completed, you can typically export the results in a format that is compatible with molecular visualization software like Discovery Studio Visualizer. SwissDock may provide options to export the results in common file formats, such as PDB (Protein Data Bank) or MOL2 (Molecular 3D Structure) files.

Open in Discovery Studio Visualizer: Launch Discovery Studio Visualizer on your computer. Use the "Open" or "Import" feature in Discovery Studio Visualizer to load the exported SwissDock results. This usually involves selecting the PDB or MOL2 files containing the protein-ligand complex structures.

Analyze Ligand Interactions: Once you have imported the SwissDock results into Discovery Studio Visualizer, you can analyze the ligand interactions with the protein target. You can visualize the binding modes, hydrogen bonds, hydrophobic interactions, and other molecular interactions between the ligands and the protein.

Visualize and Interpret: Use the visualization and analysis tools in Discovery Studio Visualizer to examine the docking results, study the ligand-protein interactions, and gain insights into the binding affinity and stability of the complexes.

Dear Emily Fr ,

You will know, that mKeima is a fluorescent Protein to measure the intracellular pH. As you can find here:

Excitation should be around 440 nm and you will have to measure two emission wavelengths i.e. 650 nm and 600 nm, but I have never used mKeima. Standard GFP and mCherry filters will not work, since a GFP filter might be able to excite your protein the band pass filter for the emission will not match the emission of the mKeima. And mCherry Filters will not excite your protein although they should have the right emission filter. If you have the possibility to use a long pass filter for the emission you might be able to check the transmission. Or you can try to use the emission filter of the mCherry Filter set in the GFP Filter set instead of the existing GFP Emission Filter, but that might depend on the dichroic filter.

Best wishes

Soenke

run EEGLAB

in command line write:

M = csvread("path/file.csv")

then import the "M" array using the menu "File > Import data > From ASCII/float file or Matlab array"

Don't forget to enter the sampling rate and specify 'M' as a variable to import.

It is something caused by intense eye use. Its main cause is looking at computer, cell phones for prolong period of time.

As any weariness, degree of visual fatigue depends on spectra of eyes loads (stress). So, for person working in different media the dynamicis of this process with time is changing .

I believe it's more about how you use the tool rather than which tool. But Matplotlib can do quite well for many common requests. They have a library of examples where you can learn how to create more complex plots: https://matplotlib.org/stable/gallery/index.

There's seaborn (developed based on Matplotlib) as well, if you want something to be done real quick, try that. I'd suggest you start with Matplotlib first, understand their principles, and then move on to seaborn, where customization requires you to add more Matplotlib parameters. See the gallery here: https://seaborn.pydata.org/examples/index.html.

There's also plotly (https://plotly.com/examples/), suitable for developing interactive apps. It looks really potential, but is an independent platform from Matplotlib, so will take you a while to learn.

I really appreciate your help , thanks :)

Samir Ranjan Panda

I think this band is because of the primer bind with itself we call it dimer

I don't have first hand information, but I would think VR will be a good way to give people an experience of driving the vehicle before buying the car.

Welcome

ad 2)

Use this calculator

A good question. The answer is not that simple. But I will try. Please look at the videoclip (at 589 sec.) in which all functional perception processes within the grasping of a coffee cup is explained. Do you understand the commonalities between the alarm clock and the coffee cup?

Please let me know and then we discuss this further.

Dear Patrik Silva, it depends mostly on your specofic task and your aesthetic sensitivity. Feel free to use your own color scheme that most suitable to your goal. For example, use contrast colours if you need to unveil some thresholds, etc.

Lida Ghaffari To access version 4.5 of the Discovery Studio Visualizer software, you can follow these steps:

1. Visit the official website of Discovery Studio Visualizer or the software's vendor website.

2. Look for the "Downloads" or "Product Downloads" section on the website.

3. Find the version 4.5 of the software in the available downloads. It may be listed under previous versions or archives.

4. Click on the download link for version 4.5. If the link requires registration or login, proceed to the next step.

5. Fill in the registration fields with the required information. Ensure that you provide accurate details and complete the registration process.

6. After completing the registration, you should receive access credentials or a confirmation email.

7. Log in to the website using your newly created account or the provided credentials.

8. Locate the download link for version 4.5 again and click on it to initiate the download.

9. Follow the installation instructions provided with the software package to install Discovery Studio Visualizer version 4.5 on your system.

If you encounter errors or difficulties while filling in the registration fields, ensure that you have entered the correct information. Double-check the required fields and make sure to provide all the necessary details. If the issue persists, consider reaching out to the software vendor's customer support for further assistance. They should be able to provide guidance on accessing the specific version you need and help resolve any registration-related problems.

Please note that software availability and access procedures may vary over time, so it is advisable to visit the official website or contact the software vendor for the most up-to-date information regarding the download and installation of version 4.5 of Discovery Studio Visualizer.

Masih -

I think what you really want to know is "how much" heteroscedasticity you have. A "test" that says you do or don't have it would be triggered by what effect level? and Impacted by sample size how? What would you do if you 'found' heteroscedasticity?

When you use a log transformation you complicate interpretation. And your model is more complex than what I want to show you. But what is below may give you some ideas:

In a regression, we can expect heteroscedasticity where variance increases with increased predicted-yi (the size measure). Modeling the heteroscedasticity in a weighted least squares regression can work very well. You might consider the following:

https://www.researchgate.net/publication/320853387_Essential_Heteroscedasticity, as bounded by Ken Brewer,

https://www.researchgate.net/publication/333642828_Estimating_the_Coefficient_of_Heteroscedasticity,

and

Knaub, J.R., Jr.(2021), "When Would Heteroscedasticity in Regression Occur?"  Pak. J. Statist., Vol. 37(4), 315-367, https://www.pakjs.com/, 

https://www.researchgate.net/publication/354854317_WHEN_WOULD_HETEROSCEDASTICITY_IN_REGRESSION_OCCUR

Best wishes - Jim

Of course, you must be winding me up, yah? :-)

There could be several reasons why you are having difficulty visualizing the EGFP protein under the microscope:

Regarding the negative western blot results, it is possible that the antibody you are using is not detecting the EGFP-CagA fusion protein. You may want to try using a different antibody or optimizing your western blot protocol to increase sensitivity. Alternatively, you could try using a different method for detecting the protein, such as immunofluorescence or flow cytometry.

These video playlists might be helpful to 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.

Find the doc attached