Images - Science topic

Andreea Iosageanu The particles i am using are microbeads .. which needs some liquid to maintain its shape , but they keep swimming in that liquid droplet.

Can you clarify the description of the problem? Your question was asked in a very general way.

I am looking for collaboration with microbiologist globally.

If you interested we can do it.

With regards.

We can support that image as you want.

Please connect with me by mail.

Dear Doctor

Go To

[Abstract

If you read that quantum machine learning applications solve some traditional machine learning problems at an amazing speed, be sure to check if they return quantum results. Quantum outputs, such as magnitude-encoded vectors, limit the use of applications and require additional specifications on how to extract practical and useful results. In satellite images, degradation of image contrast and color quality is a common problem, which brings great difficulties for information extraction and visibility of image objects. This is due to atmospheric conditions, which can cause a loss of color and contrast in the image. Regardless of the band, image enhancement plays a vital role in presenting details more clearly. The research includes new computational algorithms that accelerate multispectral satellite imagery to improve land cover mapping and study feature extraction. The first method is a quantum Fourier transform algorithm based on quantum computing, and the second method is a parallel modified data algorithm based on parallel computing. These calculation algorithms are applied to multispectral images for improvement. The quantum-processed image is compared with the original image, and better results are obtained in terms of visual interpretation of the extracted information. Quantitatively evaluate the performance of the improved method and evaluate the applicability of the improved technology to different land types.]

The deconvolution plugin in IHC Profiler gives DAB-stained images separately from hematoxylin. same as how you mentioned I use the threshold function to quantify nuclear DAB. The final scores range from 1-4. 1-negative and 4- strong positive. This is based on percentage positivity and intensity. you can don't need to correct for hematoxylin as it is already deconvoluted for nuclear DAB.

LSTM (Long Short-Term Memory) is commonly used for sequential data, including time-series datasets like EEG signals, as well as in Natural Language Processing (NLP) and speech analysis, because these types of data are treated as consecutive sequences. For image classification, deep learning models are among the most widely used techniques. Pre-trained models such as VGG19 or ResNet can be applied directly or combined to create ensemble models, which use multiple pre-trained models together. Integrating an attention mechanism with a deep learning model can further improve the feature extraction process.

5% pen-strep is too much for most of cell lines. It might cause toxicity due to cell stress and affect the growth. You may want to consider change it to 1%

Dear Salma Abdelmaboud ,

To edit Immunohistochemistry (IHC) images, you can use several software options that are suitable for scientific image processing, among the following.

(1.) ImageJ/Fiji (https://imagej.net/software/fiji/downloads): This is a powerful, open-source image processing program that is widely used in scientific research. You can use plugins to enhance images, remove artifacts like bubbles, and change background colors. The “Color Deconvolution” plugin can help you separate stains, and you can adjust the background using the “Enhance Contrast” feature.

(2.) Photoshop(https://www.adobe.com/products/photoshop/free-trial-download.html): Adobe Photoshop is a professional image editing tool that provides advanced features for editing IHC images. You can easily remove unwanted bubbles using the clone stamp or healing brush tools, and you can change the background color using layer adjustments.

(3.) GIMP(https://www.gimp.org/downloads/): Similar to Photoshop but free and open-source, GIMP offers a range of editing tools. You can use the healing tool or clone tool to remove bubbles and adjust colors using the “Colors” menu for background modifications.

(4.) BioImage Suite(https://www.slicevault.com/): This software is tailored for biological image analysis and provides tools specifically for processing and analyzing IHC images.

Note: Make sure to keep a copy of your original image in case you want to revert to it later.

Hope this helps,

Shafik

By experience, all III-V compounds will produce this type of contrast when doing a FIB x-section. The contrast you see is produced by droplets of Ga for GaAs or In for InP. The only way to get rid of them would be to reduce the dose, therefore the current and time used for the final polishing of the area. On the other hand, it helps to identify different combinations of III-V layers because every material has a slightly different contrast. Here 2 papers that explain the process:

To simplify the challenge of colour image completion, we use well-established matrix completion techniques to unfold the tensor into a matrix. Application of optimisation techniques and cross-channel reconstruction of missing pixels are facilitated by matrix completion, which is computationally more efficient and takes advantage of the low-rank structure of images.

Upgrade your Ubuntu to 24.10 via Software Updater, or install lower version of Ubuntu. This error happens only on 24.04.

Tom A Campbell I was suspicious because cyan was used in the image. Cyan is the opposite color of red. It made me think of an after image resulting from staring at colors.

(See attached image:)

When you stare at the center black dot of the left image for a while, and then switch to the white image on the right, you will see the complementary colors.

You can find a better Coca-Cola image and some more examples on following Japanese site. The page is loading a bit slow but it is worth the effort to take a look:

The bands on the gel are not continuous. You still see the boundaries between the 6 samples, they have just migrated closely together. This is a normal effect of SDS-PAGE, since the differences in salt concentartions and pH between the gel and the samples lead to a broadening of the samples (electric current pulling the proteins not only downwards, but also sidewards at the edges), which is usually stooped when the sample from the neighboring well interferes with sidewards movement. If you need to have free space beween the samples, leave one lane free (or better fill it with empty loading buffer) between the samples.

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

You can assess apple yield using satellite imagery by employing UAVs with deep learning for fruit detection and analyzing multispectral images with machine learning models. Additionally, monitoring growth patterns through time-series data can enhance yield predictions.

For image reconstruction, particularly in medical imaging, GANs can be effective, with several variants such as CycleGAN, Pix2Pix, SRGAN, and WGAN-GP showing good performance for reconstruction tasks. However, newer models like Diffusion Models, Transformers for Image Generation with Diffusion, and Vision Transformers (ViT) are often better choices for achieving high-quality and stable reconstructions.

Here are some references you may find helpful:

1. https://github.com/lihkinVerma/Image-Reconstruction-by-Vision-Transformer

Kind Regards, Md Foysal Ahmed

To use an image from a scientific paper in your educational YouTube video, you need to ensure you have permission. First, check if the image is under a Creative Commons license, which allows for reuse with proper attribution. If not, you should contact the publisher or author for permission. When you use the image, include a citation in your video description and on the image itself, mentioning the source, author, and publication. This way, you respect copyright laws and give proper credit to the original creators.

With “modern” and its cognates (pre-modern, early modern, late modern, modernity, modernism, postmodern, etc. etc.) being bandied about so much these days, and with no general agreement as to the time periods or ideological scope falling under such designations, a few words to the unwise in the subtext to the question would have been helpful. Some would argue that the Third Reich was an antimodernist reaction to modernity, much as Islamism today. Others would claim that these reactions constitute a reactionary modernism, insofar as it adopts the technologies of modernity without giving up premodern conservatism. As they say, it's complicated.

Sarada K Gopinathan , thanks, i will sent for you

First of all, we have to consider the possibility of averaging a stack of several captures (>10) to eliminate temporal noise. So when I say "image", I mean the average of the stack. In this way, we can minimize the problem of spatial non-uniformity. What we have to do is:

1) to obtain an image in darkness, I'll call it DSNU;

2) to obtain an image in front of a Flat Field at the end of the scale in the dynamic range of the instrument, but making sure that there is no pixel in the image that enters into saturation; I'll call this FF;

3) to obtain PRNU = FF - DSNU;

4) to obtain the average value of PRNU, I'll call it PRNUm;

5) and considering that we already have the reference image, a raw image, which I'll call RAW;

6) finally you can obtain your corrected flat field image (Icorr), assuming a linear behavior throughout the entire dynamic range of the sensor, as follows: Icorr = (RAW-DSNU) x PRNUm / (FF - DSNU).

Regards!

Dear Doctor

Go To

[Abstract Commercial information technology has changed dramatically over the past decade, with profound consequences for both software developers and software consumers. Software-as-aservice (SaaS) enables remote use of powerful capabilities, from accounting and payroll to weather alerts and transporation logistics, that used to require expensive in-house facilities and expertise. Platform-as-a-service (PaaS) offerings from cloud providers simplify the development and operation of SaaS software. These developments have slashed costs, reduced barriers to access and entry, and spurred innovation. Science cyberinfrastructure, in contrast, seems stuck in the 20th Century.

Summary With effort and vision, we can ensure that a broad spectrum of software is accessible in this way, greatly simplified due to outsourcing of functionality and sustained by a broad community of subscribers. We will thus promote values we hold dear, such as accessibility, reproducibility, and more money for research.]

Dear Prithiviraja Pooventhan ,

Yes, combining a Feature Pyramid Network (FPN) and Atrous Spatial Pyramid Pooling (ASPP) in a single network can significantly enhance feature extraction, especially for tasks like object detection and semantic segmentation. FPN excels at capturing multi-scale features by creating a pyramid of feature maps that incorporate both high-level semantics and low-level spatial details, allowing the model to handle objects at different scales more effectively. On the other hand, ASPP leverages dilated convolutions at multiple rates to capture contextual information over various spatial scales without reducing resolution. By applying ASPP on top of FPN’s multi-level feature maps, the model can benefit from both rich local detail and broad contextual awareness, leading to improved performance in recognizing objects or patterns across diverse scales and contexts. This combination strengthens the network's ability to detect fine details while also capturing long-range dependencies, making it a powerful architecture for complex vision tasks that require both precise spatial resolution and comprehensive global context.

% Define image parameters (you should know these in advance)

width = 512; % Image width (in pixels)

height = 512; % Image height (in pixels)

bitDepth = 8; % Image bit depth (e.g., 8, 16)

% Open the .raw file

fileID = fopen('image.raw', 'rb');

% Read the image data

rawData = fread(fileID, width * height, 'uint8'); % 'uint8' for 8-bit images

fclose(fileID);

% Reshape the raw data into a matrix form

imageData = reshape(rawData, [width, height]);

% Display the image

imshow(imageData, []);

% Display the image to allow user to select a line

imshow(imageData, []);

% Use improfile to extract intensity profile along a line

intensityProfile = improfile(imageData);

% Plot the intensity profile

figure;

plot(intensityProfile);

title('Intensity Profile');

xlabel('Position along the line');

ylabel('Intensity');

To quantify fluorescence intensity from DCFH-DA staining (or any fluorescence-based assay) using ImageJ, follow these steps:

Step-by-Step Guide:

here they are, outlined in blue

Removing the interferometer block was an unnecessary and dangerous way to resolve this issue. The solution was quite simple.

Gun Alignment

Yes, it can, like it can for a range of primary and tertiary stakeholders. To a certain extent, it depends on how involving and consultative management are with their stakeholders as to the level of brand strengthening and authentic value it creates for the brand .

Dear Anja,

If I understand your protocol in Image 2 (PCRproduct.png) correct. You are using 25 ng gDNA in your PCR. That is way to low. You should think about copy numbers. Of cause 25 ng plasmid has millions of of copys in there, while in one genome your AAVS1 knock in might exists only once. Please try to use 300-500 ng for your gDNA along with your low plasmid DNA concentration.

And than you should optimize your PCR condition subsequently. You should not really see any products in 1 and 2 both lane 1-3.

And after doing comparable stuff with TALEN in Hek293 cells. If your are planing to use single cell clones. You can use both outside primers to detect if your cell is homo or heterozygous.

Best wishes

Soenke

Dear Dibbo Dey ,

The processing of brain signals for Motor imagery (MI) classification to have better accuracy is a key issue in the Brain-Computer Interface (BCI). While conventional methods like Artificial neural network (ANN), Linear discernment analysis (LDA), K-Nearest Neighbor (KNN), Support vector machine (SVM), etc. have made significant progress in terms of classification accuracy, deep transfer learning-based systems have shown the potential to outperform them. BCI can play a vital role in enabling communication with the external world for persons with motor disabilities.

Regards,

Shafagat

DICOM (Digital Imaging and Communications in Medicine) images are widely used in the medical field for storing and transmitting medical imaging data. However, the security of DICOM images is a significant concern due to the sensitive nature of the data they contain. Here are some key aspects related to the security of DICOM images:

1. Inherent Security Risks

2. Transmission Security

3. Storage Security

4. Regulatory Compliance

5. Advanced Security Measures

6. Emerging Threats

DICOM images are not inherently secure, but with proper implementation of encryption, access control, and compliance with regulatory standards, the security of DICOM files can be significantly enhanced. The ongoing evolution of threats and security practices means that continuous vigilance and adaptation of security measures are necessary to protect these critical medical data assets.

Line scan is mostly taken across interfaces. Matrix-precipitate, Matrix-grain boundary phase etc.

Prior to that, you must have an idea of the elements present in both matrix and precipitate. In order to get an idea, take point scans on precipitate and adjacent matrix (3 points each would be good, statistically).

Analyze the elements and take line scans with the desirable elements.

PS: For line scans, use dark colours.

You can take size etc with ImageJ, yes. Here is procedure https://youtu.be/zrGm-gBhsUQ

Vahid Abbasian Thank you for helping me

Dear Petro V. Kuzyk, find attached the video to know the way i crop removed and inverted background color instead of black to white and then quantified the IHC DAB staining.

you may please send me demo video of the ways you suggested above. particularly with the ROI manager method.

Update: DAPI concentrations of 1:3K and 1:6K for 15 minutes were much less effective compared to 1:1K, so I decided to stick with the 1:1K concentration. Adding antibodies did not interfere with the perfect staining achieved at 1:1K. Thanks again to everyone who provided feedback!

Vahid Abbasian Thank you for your answer, sepas.

I recently made map contact diagram through one of the tools (chart) in Discovery Studio software.

regards

I searched for you question and get this result:

Yes, there are several methods for unpaired image-to-image translation beyond Generative Adversarial Networks (GANs). While GANs are popular for this task, other approaches can be effective as well. Here are some alternatives:

CycleGAN: While it is based on GAN architecture, the CycleGAN specifically addresses unpaired image-to-image translation by introducing cycle consistency loss, ensuring that images can be transformed back and forth between domains.

Attention Mechanisms: Some models utilize attention mechanisms to focus on relevant parts of the image during translation. These can be combined with traditional neural networks rather than GANs.

Variational Autoencoders (VAEs): VAEs can be applied in unpaired scenarios by learning a latent space that can represent images from both domains. Variational methods can facilitate smooth transitions between the domains.

Neural Style Transfer: Techniques based on neural style transfer can transform images by applying the style of one image to the content of another. While not a direct method for image-to-image translation, it can be adapted for unpaired tasks.

Domain Adaptation Techniques: Some approaches, such as Domain Adversarial Neural Networks (DANN), use adversarial training to align the features of the source and target domains, allowing for translation without paired examples.

Self-Supervised Learning: Self-supervised methods can leverage unlabeled data from both domains to learn suitable mappings without requiring pairs.

Feature Matching Loss: Instead of adversarial losses, this approach compares the feature representations of images in both domains, guiding the translation process based on the closeness of the learned features.

Graph-Based Approaches: Some methods utilize graph-based techniques where images are treated as nodes in a graph, allowing for relationships and translations to be learned more flexibly.

Diffusion Models: Recent advances in diffusion models have shown promise in generating images and can be explored for unpaired image translation.

Each of these methods has its strengths and trade-offs, and the best choice often depends on the specific requirements of the task at hand and the nature of the datasets involved.

Hello Howard,

Thanks a lot for your reply! I used VECTASHIELD® Vibrance™ Antifade Mounting Medium in both cases, so it probably isn't affecting the staining. I agree that after IHC, it looks more like autofluorescence. More importantly, your advice to increase the concentration of DAPI and the duration of incubation worked perfectly. I tried a 1:1000 dilution for 15 minutes and need to titrate it down to see if I can use a lower concentration. The full set of additional checks I performed is described in this discussion (https://www.researchgate.net/post/Weak_DAPI_staining_after_immunohistochemistry#view=66b38572b7e618ee9e041db8/1), and here I’m attaching the final images as of now. Thank you so much for your help, I really appreciate it!

1) The resolution of a SEM is specified by the vendor for certain conditions (e.g. 30kV) on a test sample (e.g. Au on carbon). If you use a SEM the resolution was demonstrated during installation, after maintenance by the service! For lower beam voltages the resolution values may be worse (more nm, some nm). You should find values in the technical data sheet. It's challenging to get the specified values, but it's a good training (a standard sample is required).

2) You can't expect to get the best resolution values on every sample! Limiting factors are electron scattering, roughness, composition, layer thickness, detector....

Malcolm White Could you please show me and detailed example? I have attached one sample of my datasets here.

My experiment was performed to collect data from 0 -360 degree with 10 degree increment.

Hussein A. Jasim Thank you so much for your recommendation. I stuck at how I can use my collected data to reconstruct the image. because VNA is collected data from 0.4GHz to 4GHz; on top of that I rotated 360 degree around the object, so I have about 37 datasets int total. I attached one of my sample dataset below. Please help me

Thank you dear Jokin Ezenarro,

The purpose of the images is for training purposes and not for research purposes.

Dear Wedad,

you can find multilayered Ti3C2Tx MXenes XRD patterns in these papers:

Your XRD pattern has some similarities to MXenes; however, the MAX phase quality and pretreatment can significantly influence your synthesis products. I suggest using Raman spectroscopy and scanning electron microscopy to investigate the synthesis products and ensure MXenes' quality.

The ability for AI to not only generate visuals but also impact perception offers a huge leap in both technology and our understanding of cognition. This might transform industries like art, neuroscience, and even human-computer interaction, pushing the boundaries of how humans create and perceive visual information.

Kamel Ghanem Ghalem if you mean reduce the size of the image without using imresize command:

I = imread('lena.png'); % Replace 'lena.png' with the path to your image file

% Convert to grayscale if it's a color image

if size(I, 3) == 30

I = rgb2gray(I);

end

% sampling the image by a factor of 0.5

samp=(1:2:end, 1:2:end);

% Display the original and downsampled images

subplot(2, 1, 1);

imshow(I);

title('Original Image');

subplot(2, 1, 2);

imshow(samp);

title('Downsampled Image');

Fatemeh Yaghoobiadeh No. never added protease inhibitor

Dear Manoj Kumar Srivastav ,

Previously, we described how to perform correlation test between two variables. In this article, you’ll learn how to compute a correlation matrix, which is used to investigate the dependence between multiple variables at the same time. The result is a table containing the correlation coefficients between each variable and the others.

Regards,

Shafagat

Differing projections of the sensor optics. You can correct with a positional warp. This can be done by matching associated points and using a polynomial offset in X and Y. OpenCV also contains homography methods which can align different sensors.

The choice of approach totally depends on the specific dataset, available resources, research objectives, and most importantly the desired level of accuracy. You can experiment with different methods to find the one that best suits your needs.

There are several approaches you can explore to detect anomalies in image datasets. You can explore different machine and deep learning algorithms, such as Autoencoders, Isolation Forests, Support Vector Machine, Convolutional Neural Networks, and Contrastive Learning. Additionally, you can explore hybrid approaches that combine machine learning, computer vision, and deep learning techniques to extract relevant features from images to detect anomalies.

¿Qué opinan ustedes?

If you include a representative image, this might be an easier question to answer. Based on your description, I would hazard to guess that your cells are too dense and possibly overlapping for thresholding and watershedding to work well. There are a couple of other things that you can try.

Cell Counter plug-in: https://imagej.net/ij/plugins/cell-counter.html

If you are using multiple colors, you can try isolating the channels and then trying to threshold and watershed. If your cells are dense then you should take overlap into account. You can look for this by using the Image Calculator “AND” function.

Depending on their morphology, the Binary Feature Extractor may also work.

There are more options out there if these don't work, but these may get you started.

Hope it works!

- Melissa

Dave,

Saw your 2D line by chance; nicely complex!

I concur with Jim that post-stack cleaning up of the lines would be helpful. Different interpretation platforms provide different tools. I would recommend filtering in the FK domain. Are the lines migrated?

A comment, the shallow overburden does not contain spark of high reflectivities, supporting an acoustically relatively homogenous interval. Note that you have limited fold at shallow times, i.e. the stack is from a limited number of traces there.

The seismic contains reflective and non-reflective sequences; have an acoustic impedance log and synthetic made of a well if possible. As a rule of thumb, reflective packages relate to sequences with contrasted lithologies, i.e. carbonates interbedded with shales or volcanics with clastics. Transparent units are likely to be more homogenous.

Keep fault geometries simple.

Try to interpret some horizons across the sections and flatten - unflatten the section along those horizons, it may help you figure out what the seismic contains.

Shallow heterogeneities may lead to a cascade of poor imaging, as do fault zones. Imaging of faults on 2D seismic lines running oblique to faults are challenging for fault imaging.

Good luck with your project.

Thomas.

Dear Aqsa Ahmed

i tihnk that the problem could be related to the fact that you invert the colours in the step 3. Try to repeat the analisys avoiding this step.

in the following link you can find an example of my analysis approach.

best

Manuele

The main phase matches, but there are additional minor phases. You may check the XRD pattern of another phase with a similar composition.

read this article

From my limited knowledge hemoglobin spectra in visible spectrum, the peaks of every sample correspond nearly exactly. It seems that the difference seen is due to differences in concentration only. Further you have not mentioned the abnormal conditions in each individual. Please give some more details of protocol applied and the working hypothesis of your study with relevant references. I have no personal experience of this research methodology

looking like erythrocytes

Have you already solved the problem of cardiac interval extraction? Try to understand how this is done in open source software. For example

Yes, there are techniques for determining or choosing the dimension of a Self-Organizing Map (SOM):

Choosing the right dimension ensures that the SOM effectively captures the underlying structure and patterns in the data without being overly complex or sparse.

Dear Eliana Fuentes

When you mention that "the data the plot deviates further from the actual data", refer to how, for example, the maxima at 2θ 5° is not displayed, or this maxima is not included in the Fullprof refinement, or both.

In principle, the boundaries of the diffraction pattern to take into account to be "refined" and "displayed" in the Rietveld refinement with Fullprof (shown in the image) should be changed. There are two different concepts, the first is the most important because it directly affects the refinement. The second has to do with the aesthetic aspect, to visualise the fit in the moment.

In the first case, if the refinement does not take into account important regions of the pattern, it could be solved by changing the 2θ boundaries of the pattern to take into account to be refined.