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We are trying to pick up an effect on lysozyme by measuring the absorbance of florescent labeled M. luteus cells at OD600. We are using this as a substrate because we are comparing the results we observed using this substrate in an enz chek kit. We are getting very wavy lines(see attached) in the reading. Anyone have any suggestions as to why?
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The waviness of these lines may just be due to the limited number of pixels available in the display. If you plot the data in a graphing program, such as Excel, are the graphs also wavy?
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I would like to run the FAM labelled oligo DNA on a agarose gel and detect it with Biorad Chemidoc MP, it has "Nucleic acid gels" option, "Protein Gels" and "Blots" Fluorescein channel. Which should I choose as my gel is not stained with anything but only the DNA is labelled with FAM, I wonder if I should still choose nucleic acid gels option.
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Emily Tsui You can choose Nucleic Acid application and pick SYBR, because the FAM molecule and SYBR have a similar ext/emm wavelength.
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Hi all!!!
I have been trying to quantify the DOTA on my labelled protein but did not get any success. What method is best to quantify the DOTA labelling? If anyone has prior experience with similar research, please reply so I can discuss and understand where I am making a mistake.
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Dear Jitender
If it is feasible will you be able to share the MS.
Thanks
Rakhee
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I`ve read the your article Comprehensive microRNA-sequencing of exosomes derived from head and neck carcinoma cells in vitro reveals common secretion profiles and potential utility as salivary biomarkers. Oncotarget 2017.I found it interesting and I’m now curious and willing to explore your GEO data (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE84306), because I’m developing a study in the same area.However I can’t understand the labeling in the chart attached down bellow. In the GEO display, I can see the individual samples as follows:
GSM2231252H413_1
GSM2231253H413_2
GSM2231254H413_3
GSM2231255...
But I can’t relate them to the labels presented in the chart attached. For example, what does SL1.counts mean? Does it stand for H413_1_ GSM2231252? Are there replicates? Can you help me understand why the crude data values and adjusted values are the same?
Could someone helpe me with that? Athours did not have answer me...
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Scott, I am very grateful for the contribution of information, it will be substantial for the completion of our work!
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I need to permanently label 38 metal cryo racks, and I'm interested in the solutions that you may have already arrived at.
Marker pen fades after about a year, stickers crack and fall off. The most durable thing I have found so far is metal cable and metal tag, but I'm concerned about these betting jammed in the carousel that holds the racks.
Any suggestions?
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For labeling cryo racks, I recommend using laser engraving directly on the metal surface of each rack. This method offers a permanent, highly durable solution that won’t fade, crack, or interfere with the carousel mechanism. If laser engraving is not an option, another alternative is using high-temperature-resistant adhesive labels specifically designed for cryogenic storage. These labels typically withstand extreme temperatures and are less likely to peel or crack. Combining these labels with a protective clear coating can further enhance their longevity without causing jams in the carousel.
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My extracted RNAs are biotin labelled. And I have used Biotin-Aniline to label RNAs. I want to store these RNAs at -80 degree Celsius for next day processing with streptavidin conjugation.
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I'm working with Weka using KDD Cup 1999 dataset. So I've got a few questions I couldn't figure out via manuals:
  1. How do we know what parameters for Ranker to set? I mean, threshold and numToSelect. Is there any explaination to this?
  2. When I select attributes via explorer and save the modified dataset, it's always N+1 attribute (N selected attrbutes + class/label). Why? Isn't a label/class also an attribute?
  3. Why when I use PCA+Ranker with default settings for attribute selection I get more attributes than I had?
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Please find the answers below.
1. How do we know what parameters for Ranker to set (threshold and numToSelect)?
Answer
The Ranker method in Weka ranks attributes based on their importance and allows you to select the top attributes based on either a threshold (cutoff value) or a specific number (numToSelect).
  • Threshold: This parameter sets the minimum acceptable ranking score for attributes. Attributes with a score below the threshold will not be selected. If you're unsure what value to set, it's often useful to start with a small threshold (e.g., 0) and experiment by gradually increasing it.
  • numToSelect: This parameter specifies the exact number of attributes to retain. In large datasets like KDD Cup 1999, selecting only the most relevant attributes can help streamline your model. By reducing noise and complexity, you may improve model performance. A good starting point is to experiment with retaining 10-20% of the total attributes and adjust based on the model's performance.
2. When I select attributes via explorer and save the modified dataset, it’s always N+1 attribute (N selected attributes + class/label). Why?
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Your question is not clear, you may need to elaborate on it.
3. Why, when I use PCA+Ranker with default settings for attribute selection, do I get more attributes than I had?
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By default, PCA in Weka is designed to generate enough components to capture a large percentage of the variance in the data (e.g., 95%). This can result in more components than the original number of attributes because PCA aims to retain as much information as possible. Even though PCA typically reduces dimensionality, if the goal is to capture a high level of variance, it might generate more components to achieve that.
I hope that helps.
Kind regards,
Dr. Samer Sarsam
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How we Detect it after labeling indirectly?
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Detecting heptane indirectly after chemical derivatization (coding) typically involves converting heptane into a more detectable form, such as a product that is more reactive or has a distinct spectral signature.
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After being modified with DBCO-PEG4-NHS and subsequently labeled with a secondary antibody (targeting the Fc region), the antibody was found to be unable to bind to antigen-expressing cells. What could be the possible reasons for this
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Actually; Every antibody is specific for the specific antigen, Any antibody which is for specific antigen that will no work or bind with other antigen, because light chain of antibody have specific site for antigen and when we modify antibody it will lose its specific binding site , so ,you may use alternative antibodies or may there an error with the procedure of doing and change the experimental condition for working
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Hi everyone,
I’m currently working on flagella assembly and want to localize the FliN protein in my bacteria. We’ve already attempted to label the protein with eGFP at both the N-terminus and C-terminus, but the fluorescence appears to be cytoplasmic. We observed similar results when using NeonGreen at the N-terminus.
Do you have any recommendations?
Thank you very much!
Francois
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Maybe the protein doesn't get sorted properly when there is a large protein tag on it. It might work better to use a small tag such as the ReASH tag system.
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I am intrested how to prepare 5' labeled primers (6-FAM, NED and HEX)? Specifically, do this primers that come in powder form need to activate for some time after I disolved them before I prepare working solutions out of them?
Thank you.
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Thank you!
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Q 1. Why does deuterium labeling experiment during CH activation?
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The replacement of a proton with deuterium is done to study the isotope effect in a chemical reaction. They study the replacement of a proton with some functional group, and then replace deuterium and compare the activation energies of the reactions.
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Dear All,
Please let me know, that can be we use the dietary supplement ingredients for manufacturing and labeling of drug products.?
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Using dietary supplement ingredients in drug manufacturing requires careful regulatory navigation.
Dietary supplements and drugs are distinctly regulated; supplements don't need FDA approval before marketing but must be safe and truthfully advertised. Drugs, however, require rigorous FDA approval demonstrating safety and efficacy through extensive testing.If an ingredient from supplements is used in a drug, it must undergo clinical trials to prove its effectiveness for the intended therapeutic use.
Labeling standards also differ significantly; drug labels need detailed medical information, whereas supplements have more general nutritional information.Misclassifying a product can lead to severe regulatory penalties. It’s crucial to consult regulatory experts or legal counsel to ensure compliance with all applicable laws when considering such development.
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there is an uploaded image of correlated matrix. please help what types of this dataset will be treaded labeled or unlabeled.
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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
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What does ‘preventing carryover contamination’ mean?
If they are used, is pre-treatment with UDG necessary?
Thanks :)
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In the context of PCR and DNA amplification, 'carryover contamination' refers to the accidental transfer of previously amplified DNA (amplicons) into new PCR reactions. This contamination can lead to false positives, where the detected signal is due to these contaminant DNA molecules rather than the target DNA from the sample being tested.
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We have mice brains that were over-fixed due to old PFA used during perfusion. Thus, the synapses are no longer being labeled by the Synaptophysin (SY38) mAB. which works perfectly every other time.
I have already tried antigen retrieval, but that has not been helpful either.
It is really important for us to label the synapses and we have no option but to use the over-fixed mice brains we have right now.
I am planning to try other synaptic markers like PSD95 and Synapsin next.
But I am open to more suggestions or troubleshooting ideas.
Would love to hear if someone has faced similar issues.
Thanks a lot.
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I would try VGLUT and VGAT antibody from Synaptic Systems, wich are very good in labelling synapses. Both are presynaptic markers. You can also use Homer1 (post synaptic excitatory) or Gephyrin (post-synaptic inhibitory). Also in this case, the Synapt Systems ones, works really good.
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The above are manually labeled extrinsic matrices based on the first image
It can be seen that the projection error at the edge is large, while the error at the center is small.
What could be the reason? How can I solve it
Thanks.
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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.
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I have a Polyjet that has passed its labeled expiration date for 1 year and I'm going to re-start transfection experiments. It is really needed to change it? Transfection efficiency may be lower?
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No, it is better you don’t use Polyjet once it has passed its expiry date. You need to use a fresh vial. I never encourage the use of expired reagents. You will end up with unreliable results. Moreover, the experiment may have to be repeated which will only result in wastage of time and resources.
Regards,
Malcolm Nobre
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Hello everyone!
I'm working on some problems in Natural Language Processing. Now I've been working on Stance Detection. I have an idea but I'm not sure either it's useful or not.
I want to use RTE data (labels: "entailment", "contradiction", "neutral") for training Stance model. I think "entailment" and "contradiction" labels in RTE and "support" and "deny" labels in Stance are equivalent respectively.
I wonder if you think so. if not please give me some reasons for it.
Thanks alot in advance.
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Hi, Masoud Sadeghi
Yeah you can investigate more for the best insightful outcomes!
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While preparing fluorophore labeled probe via nick translation, would we obtain smear or should there be a single band of the same size as the template?
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We will get smear between 300-600 bp bands.
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I am doing research on the effects of labeling and stigmatization of teen mothers as deviant and if it has negative or positive impact on their sense of self. Also, what variables contribute to the labeling of teenage pregnancy deviant? All input is appreciated and welcomed.
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Pregnancy can never be deviant, because it is the law of nature. However, it depends on your economic situation, i.e. your resources and support to raise the child. Generally, society will punish you and the child, because you should have waited to have available resources to bring another human being being into this (unjust) world.
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My goal is to see two kinds of particles mixed well in an aggregated form.
My experiment was as follows:
1. Prepared Microparticles 1 + EDC/NHS + RhoB, and Microparticles 2 + EDC/NHS + FITC.
2. Mixed them, centrifuged them, placed them on a glass slide, and then checked them with a fluorescence microscope using different filter sets.
3. Merged the image channels (green and red).
The outcome was not satisfying. There seems to be crosstalk between the particles. When the images were merged, the green and red colors overlapped, making it difficult to distinguish the particles. I troubleshooted by checking each type separately, and they both showed successful labeling (Particle 1 showed red with no green, and Particle 2 showed green with no red).
ChatGPT-4 recommended the following steps:
1. Wash each particle after labeling.
2. Use blocking agents (BSA, FBS, and Tween-20) to prevent non-specific binding.
3. Check the filter set range when measuring.
I did all these steps (including overnight blocking), but the crosstalk between particles 1 and 2 still occurs.
I would be appreciated if someone can recommend me the solution to solve this problem.
Thank you.
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Sequential Excitation and Detection (Multi-tracking):
Excite and detect each dye sequentially rather than simultaneously. This approach ensures that the emission from one dye doesn’t interfere with the detection of another. For example, if you label particles with Alexa 488 (green) and Alexa 568 (red), excite and detect them one after the other to avoid crosstalk.
Spectral Separation:
Use additional spectral information to separate emission signals. If there’s overlap between the emission spectra of two dyes, choose laser lines and detection channels that minimize this overlap. Spectral imaging systems can help with this.
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What are the conditions for labeling an Anthozoan species as an Invasive species?
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An Anthozoan species, which includes organisms such as corals, sea anemones, and sea pens, can be labeled as invasive if it meets the following conditions:Non-Native Origin: The species must be introduced to an area outside its natural range. This introduction can be intentional (e.g., through aquaculture or the aquarium trade) or unintentional (e.g., via ballast water from ships or through natural dispersal mechanisms).Establishment and Spread: The species must be able to establish a self-sustaining population in the new environment. This involves successful reproduction and spread within the new area.Negative Impact on Native Ecosystems: The species must have demonstrable negative impacts on the native biodiversity, ecosystem functions, or human activities. This can include:Outcompeting native species for resources such as space, light, and nutrients.Altering habitat structures and functions.Introducing diseases or parasites that affect native species.Disrupting ecological relationships, such as predator-prey dynamics or symbiotic associations.Human Health and Economic Impact: In some cases, an invasive Anthozoan species may also be labeled as invasive if it poses risks to human health (e.g., through toxins) or causes economic harm (e.g., damaging coral reefs important for tourism or fisheries).
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Condition: "particular mode of being of a person or thing" / "a requisite or prerequisite, a stipulation," / "state; behavior; social status"
Symptom: "a departure from normal function or form as an expression or evidence of a disease," / "a happening, accident, disease," / "to befall, happen; coincide, fall together,"
It's becoming very apparent that mental health labels and loose terms of diagnosis are starting to cost countries a fortune.
I wanted to ask about depression because in the UK, some people get told they have depression as a condition. That diagnosis on its own can gain extra financial benefits and medical support.
When it is labelled alongside other things, it seems to convert into a symptom. Which I would imagine meant it is WORSE that depression. It's depression, plus some.
However, if it is "just" a symptom, it doesn't have any financial weight, or it is extremely hard to get the right support, because other symptoms start leading professionals into focusing on groups of symptoms which are faster than ever turning into conditions/ing.
Depression used to be called meloncholia. After a guy won a nobel prize in conditioning, the world had two massive wars within years. Then it was labelled "the Great Depression"
Depression is a transitional position. It feels like it is actually impossible to be depressed as a condition. If you are long term depressed, you must surely have other symptoms and conditions causing the depression to last? Or you must have not acknowledged something about yourself that needs processing properly?
I can think of no person who is just "depressed" without a story of why they feel that way, or where its cause is from." It feels like a bad therapist to just leave people with the label "depression". Is it not medically a term for "unfinished business"?
And strange that it can provide extra financial benefits, especially when people with more serious diagnosis struggle to get that same help and support.
What is so special about depression that it can be used as condition and symptom?
It feels like a loop hole created on purpose to trap poor people and or to make a bad therapist look good.
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From the viewpoint of quantifying mental health or mood science, depression is a mood disorder that causes a persistent feeling of sadness and loss of interest, i.e. the affected person is no more able to regulate her moods and the mental state of chronification can set in. Consequently, depression is a symptom as chronifying mental and physical response to a condition, e.g. learned helplessness.
Psychologists first described learned helplessness in 1967 after a series of experiments on animals, suggesting that their findings could apply to humans. Learned helplessness leads to increased feelings of stress and depression. For some people, it is linked with post-traumatic stress disorder (PTSD).
The most common treatment is therapy, especially cognitive behavioral therapy (CBT). CBT helps people overcome these types of challenges by changing how they think and act.
Ref/
_________
You Keri Mobbs wrote:
Depression used to be called meloncholia. After a guy won a nobel prize in conditioning, the world had two massive wars within years. Then it was labelled "the Great Depression".
Skinner, John B. Watson and Ivan Pavlov, are considered to be the pioneers of modern behaviorism. You seem to mean Ivan Petrovich Pavlov, Nobel Prize in Physiology or Medicine (1904)?
The inventor of the phrase, the Great Depression, is Lionel Robbins, a British economist who lived during the Depression. In 1934, after Hoover’s tenure in office, Robbins wrote the book, The Great Depression, which contains what some historians, notably David F. Burg, consider to be the fist usage of the phrase we now use to to describe the economic meltdown on the 1930s. Although Hoover did not quite invent the term the Great Depression, he did play a role in its formation.
Ref/
An expansion of bank credit creates a boom that cannot be sustained, and the inevitable collapse of the boom is the depression. Robbins traces this boom/bust cycle, detailing the central bank policies of the 1920s. If the policy gurus at the Fed had read and understood this book, they could have spared us the economic havoc of recent years.
__________
Keri Mobbs Economics is still very blind to the workings of human psychology as it uses linear models for accounting purposes, which are unable to understand the cyclical movements of human economic activity.
The mentioned Great Depression was, imo, a full central banking failure (systems error), which is also state failure (planning agency put over the market forces).
_____
PS/
There have been three great inventions since the beginning of time: fire, the wheel, and central banking. '' The reason Will Rogers made this statement is central banking is so important because the money supply is so important.
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I am running a smart farm project, need to plant 20 sensors for 10 days and must bring them back for data labeling before inputting to the supervised ML model. Data will be collected every second so I am concerned about the amount of data which will affect the time for labeling.
We spent months doing data labeling from 9 sensors, 7 days each. We desperately need a better solution to deal with this problem.
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Data is the currency of the future.
With technology and AI slowly seeping into our everyday lives, data and its proper use can cause a significant impact in modern society.
Accurately annotated data can be used effectively by ML algorithms to detect problems and propose workable solutions, thus making data annotation an integral part of this change.
Regards,
Shafagat
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Does any one has the experience using DiD, Dil to label cells for imaging? Do they work well? Is there any other choices? I will use them to label tumor cell line and T cells, and I would like to observe the cells for 2 days.
Thanks!
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May consider using Permai fluorescence dye.
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I am expressing a membrane protein (about 15 kDa) in BL21(DE3) cells. The expression is good, and the yield is decent, but each time I checked the mass with a TOF mass spec, the mass I get is nothing close to the expected value for an isotopically labeled protein, especially with 15N. When I introduced 13C glucose, I saw an increase in mass, which shows the incorporation of 13C. This is in contrast with the 15N labeling. I've tried increasing the concentration of 15NH4Cl and eliminating rich broth, but nothing has changed thus far. Does anyone have an idea how to go around this problem? Thank you
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I figured out the "problem" with my expression. So, my protein was labeled all along but the mass was lower than the expected mass (based on the sequence) because it was missing the N-terminal methionine residue.
I ran mass spec on an unlabeled sample of the protein and this was how I realized that.
Apparently, it is very normal for E.coli to get rid of the N-terminal methionine post-translation. https://www.nature.com/articles/326315a0.pdf
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I treid to find the eigenvalues for a matrix with quaternion entires of oreder 4 or greater while using Matlab. I perforemd the following steps:
1. First I created quaternion arrays in the follwing way:
O=quaternion(0,0,0,0) % for 0
L=quaternion(1,0,0,0) % for 1
i = quaternion(0,1,0,0)
j = quaternion(0,0,1,0)
k = quaternion(0,0,0,1)
2. Next I defined a 4 by 4 matrix, corresponding to C_4 labelled by quaternion units.
A=[O i O L; -i O j O; O -j O -k; L O k O]
It gives me the following A matrix in Matlab.
A=
0 + 0i + 0j + 0k 0 + 1i + 0j + 0k 0 + 0i + 0j + 0k 1 + 0i + 0j + 0k
0 - 1i + 0j + 0k 0 + 0i + 0j + 0k 0 + 0i + 1j + 0k 0 + 0i + 0j + 0k
0 + 0i + 0j + 0k 0 + 0i - 1j + 0k 0 + 0i + 0j + 0k 0 + 0i + 0j - 1k
1 + 0i + 0j + 0k 0 + 0i + 0j + 0k 0 + 0i + 0j + 1k 0 + 0i + 0j + 0k
Now I am facing problems to find eigenvalues of A.
Can someone help me to find the eigenvalues of the matrix A while using Matlab?
Thanks
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To find the eigenvalues of a matrix with quaternion entries in MATLAB, you'll need to approach the problem differently since MATLAB's built-in functions for eigenvalue computation (eig) do not directly support matrices with quaternion entries. One effective approach is to convert the quaternion matrix into a larger real matrix that can be handled by standard eigenvalue algorithms. Here's how you can do it:
Steps to Convert Quaternion Matrix to Real Matrix
  1. Understand the Quaternion Representation: Each quaternion 𝑞=𝑎+𝑏𝑖+𝑐𝑗+𝑑𝑘q=a+bi+cj+dk can be represented by a 4×44×4 real matrix. This transformation allows us to use standard linear algebra techniques on quaternion matrices by operating on an equivalent real-valued matrix.
  2. Construct the Real Matrix: For a 4×44×4 quaternion matrix, the equivalent real matrix will be 16×1616×16. Each quaternion entry in the original matrix will be converted into a 4×44×4 block in the real matrix.
  3. Compute Eigenvalues: Use MATLAB's eig function on the resulting real matrix to find the eigenvalues.
Detailed Steps in MATLAB
Here's a step-by-step MATLAB code to perform the conversion and compute the eigenvalues:
matlabCopy code% Define quaternion units O = quaternion(0, 0, 0, 0); % Zero L = quaternion(1, 0, 0, 0); % One i = quaternion(0, 1, 0, 0); j = quaternion(0, 0, 1, 0); k = quaternion(0, 0, 0, 1); % Define the 4x4 quaternion matrix A = [O i O L; -i O j O; O -j O -k; L O k O]; % Function to convert a quaternion to its 4x4 real matrix representation quaternion_to_real = @(q) [ real(q), -imag(q), -jmag(q), -kmag(q); imag(q), real(q), -kmag(q), jmag(q); jmag(q), kmag(q), real(q), -imag(q); kmag(q), -jmag(q), imag(q), real(q); ]; % Initialize the real matrix n = size(A, 1); real_A = zeros(4*n, 4*n); % Fill the real matrix with 4x4 blocks for r = 1:n for c = 1:n real_A(4*(r-1)+1:4*r, 4*(c-1)+1:4*c) = quaternion_to_real(A(r, c)); end end % Compute the eigenvalues of the real matrix eigenvalues = eig(real_A); % Display the eigenvalues disp('Eigenvalues:'); disp(eigenvalues);
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I'm having great success using Cell Tracker red with Lactobacillus. Cell Tracker Blue CMHC and Green CM-H2DCFDA aren't working. I'm doing some trouble-shooting but I'd appreciate any insights from people who have tried these dyes on bacteria.
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May consider using Permai fluorescence dye.
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I am working on Exosomes that is derived from cells that are engineered to express scFv of Anti-HER2 antibody. I want to see its expression in TEM. Can I directly label the exosomes with the Protein L-GNP or do I need to use Anti-Anti HER2 antibody and secondary antibody then stain with Protein L GNP?
Can anyone suggest literature on Protein L-GNP immuno gold staining in TEM?
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Please follow MDPI
Journal
Researcher Anirban sengupta and Azharuddin.
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I am looking at CD4 T cell populations in B6 mice at 42dpi with influenza B virus. I do perform CD45 IV labeling just before tissue harvest. Now I am running my samples on the 5L Cytek Aurora and I see this weird distribution of CD4 staining where it looks like there are two distinct populations of CD4 (see attached image). The CD4hi population (on the far right) is primarily IV protected and the CD4lo population is primarily IV labeled.
I also stain mLN, cLN, pLN and Spleen and they all look normal. This issue appears to be exclusive to the lung
Has anyone else seen this? I have been searching in the literature but haven't found anything helpful.
Thanks!
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This CD4hi population is mostly antigen experienced (CD44+). You can also stain for CD44 and CD62L to distinguish naive vs antigen experience CD4hiD44hi CD4T cells.
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I used NHS-PEG4-Azide to label protein and temporarily store the labelled protein at 4 degrees. Is it suitable for long-term storage at this temperature?
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In general, one of the commonly used storage conditions for azide-labeled proteins or peptides is to keep them in buffer solution at a low temperature, usually at -20°C or even lower, such as -80°C. This helps slow down any potential degradation reactions and maintain the integrity of the labeled molecules. In addition, storing them in the dark can prevent any photochemical reactions that may occur... On the other hand, storing azide-labeled proteins or peptides at low temperatures in a suitable buffer solution and protecting them from light should help maintain their preservation. Its stability over time.
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Example: Africa is a label for a continent. It has no overall leader or rulership, it is devided into countries. But now we are being told Africa is trying to sue Israel. But who is Africa? If "they" win, who gets the reward and how will it be fairly distributed around the continent of Africa? Or will only some people claim to have been successful in taking a country to court, and use it to show off against other African nations? I thought in order for a continent to take a country to courts, it would have to gather the signatures of every leader of each African country? If not, how does it work?
On the other side, there's Israel, the whole country's label. But within Israel are some people doing some things whilst others are not. Why should some people of Isreal get a reputation for genocide when it was just some other people using the Isreal label? How would others do trade and judge them in the future? It's not precise enough.
I don't think it is a legal move for a continent to sue a country and I'm struggling to see how this process is allowed, who personally will gain a reward from this, who will lose what etc?
It seems like an unfair trial with unfair uses of public legal systems both locally and globally. All because of labeling again. Like "them/they" issues.
Can anyone explain this process or direct me to further information I could read?
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Keri Mobbs First and foremost, one most significant aspect you should comprehend when it comes to seeking justice in accordance with international law in relation to your question is that a continent cannot physically sue or take any country to court. In order words, in terms of international law (UN Charter and ICJ Statutes), its is only possible if there are regional organizations such as; African Union or the European Union can mediate disputes or enforce treaties or agreements in line with the legal mechanisms comprises of the member states. In this note, base on your question there is no legal case at hand because such case never exist.
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When a model is trained using a specific dataset with limited diversity in labels, it may accurately predict labels for objects within that dataset. However, when applied to real-time recognition tasks using a webcam, the model might incorrectly predict labels for objects not present in the training data. This poses a challenge as the model's predictions may not align with the variety of objects encountered in real-world scenarios.
  • Example: I trained a real-time recognition model for a webcam, where I have classes lc = {a, b, c, ..., m}. The model consistently predicts class lc perfectly. However, when I input a class that doesn't belong to lc, it still predicts something from class lc.
Are there any solutions or opinions that experts can share to guide me further in improving the model?
Thank you for considering your opinion on my problems.
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Some of the solutions are transfer learning, data augmentation, one-shot learning, ensemble learning, active learning, and continuous learning.
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I’m currently using a protein (36 kDa) which needs to be unfolded during a labelling reaction. unfortunately the protein precipitates completely upon denaturation with EDTA which chelates the zinc ions holding its structure together. I’ve tried the reaction at 37, 40, and 55 degrees Celsius all with the same issue.
The exact same protocol (37 degrees, same edta:zinc ratio, time course) works for smaller constructs of the protein. I typically unfold, reduce, and then add the labelling reagent sequentially for 50 minutes each (total 2.5 hours shaking at 37). My protein concentrations have been between 20 and 200 micromolar, and the pH is maintained at 7.8 in 100 mM ammonium bicarbonate buffer (no salt) for optimal labelling.
I need the protein to remain in solution for downstream experiments after the labelling reaction. How can I denature the protein while keeping it soluble?
The protein pI is 7.06, which may be too close to the reaction buffer, especially considering that the other constructs had pi’s at 5.3 and 6.6. I’m considering testing a higher pH, unfolding with EDTA and a detergent, and increasing the salt concentration. Ideally I’d have as low a salt concentration as possible for downstream mass spec, and maintain the pH between 7.5 and 8.5 for optimal labelling with the different reagents. I’d appreciate any feedback or advice!
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There are 2 main reasons why the protein could be precipitating: electrostatic interactions or hydrophobic interactions.
Increasing the salt concentration would probably overcome electrostatic interactions. You would have to remove the salt later, which is easy to do by passing the column through a desalting column or by dialysis. Changing the pH by several units could also help, but you have explained why this is a less desirable approach for you.
In my opinion, the more likely reason for the insolubility is hydrophobic interactions, since the interior parts of proteins are hydrophobic. You could probably keep the protein in solution using a detergent, but removing detergent later can be troublesome. There are a few detergents that are compatible with mass spec, however. The other approach would be to add a strong chaotrope, either urea or guanidine-HCl, at several molar. These are small molecules that can be removed later. You would have to experiment to find the minimum necessary concentration to keep the Zn2+-free protein in solution, especially if you don't want to completely unfold the protein, since refolding a completely denatured protein can be a challenge.
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Hello,
I am a grad student working on a project on the photisomerization of 2'-hydroxychalcone. I was instructed to create a PES surface for the triplet state. My first attempt did not produce accurate results by using the method of:
%chk=r66a20.chk
# opt=modredundant ub3lyp/6-311g(d,p) nosymm empiricaldispersion=gd3
H atom moved by 0.66 ratio and 200 degrees torsional rotation
0 3
C 4.40955200 -1.07991300 -0.54555900
C 3.26596800 -1.74755000 -0.09909700
C 2.01417800 -1.42748400 -0.63366800
C 1.89642800 -0.41766900 -1.60620700
C 3.05378000 0.22560800 -2.07096300
C 4.30412000 -0.09762500 -1.53408500
C 0.59255400 -0.09649200 -2.22324900
C -0.55116400 0.12512000 -1.55522300
C -0.64735300 0.50053500 -0.12258300
O 0.34423300 1.01373800 0.46366800
C -1.90915400 0.31643500 0.63358200
C -3.15247100 0.58942200 0.03132200
C -4.33985300 0.42672200 0.74976200
C -4.30311100 -0.00451700 2.07603100
C -3.07829900 -0.27235000 2.68958800
C -1.87966600 -0.11514800 1.97776200
O -0.66382100 -0.40167900 2.59940300
H 5.37814800 -1.33016300 -0.13247600
H 3.35032800 -2.51880500 0.65556100
H 1.14048600 -1.97116500 -0.29583500
H 2.98682100 0.98376600 -2.84181500
H 5.19191700 0.41070500 -1.88749300
H 0.55957000 -0.05444000 -3.30552000
H -1.46703200 0.04969000 -2.12553300
H -3.21091500 0.95579300 -0.98478200
H -5.29020100 0.64320200 0.27899600
H -5.22451200 -0.12796000 2.63040400
H -3.06071800 -0.60621700 3.71928400
H -0.26302800 0.16107800 1.75025600
B 10 29 F
B 17 29 F
D 9 8 7 4 F
and then I used TD-DFT:
%chk=TD_r66a20.chk
#p td=50-50 b3lyp/6-311g(d,p) guess=read geom=modredundant
empiricaldispersion=gd3
0 1
C 4.57698700 -1.53187000 -1.36424900
C 3.75481000 -1.51008200 -0.23758000
C 2.55798100 -0.80468800 -0.24377800
C 2.14632500 -0.10708600 -1.39547000
C 2.99699900 -0.13134300 -2.51984500
C 4.19414400 -0.83516100 -2.50858000
C 0.89159700 0.62999000 -1.55114300
C -0.27435800 0.70469200 -0.86532400
C -0.59792900 0.35067600 0.52831800
O 0.30513800 0.24919700 1.38241800
C -2.01433100 0.18454400 0.90698200
C -3.03855200 0.09651400 -0.06205900
C -4.36552400 -0.05278100 0.28825300
C -4.71245400 -0.11193000 1.64622500
C -3.74051600 -0.04526600 2.62493200
C -2.38264300 0.08394500 2.28222800
O -1.50585900 0.10857200 3.28772500
H 5.51156300 -2.08122700 -1.34739000
H 4.05390600 -2.04016600 0.65965800
H 1.94681000 -0.76569400 0.64361800
H 2.70339300 0.40370600 -3.41709000
H 4.82662300 -0.83984000 -3.38890700
H 0.83873200 1.11664200 -2.52366400
H -1.06583700 1.23983900 -1.37662000
H -2.77550200 0.12198400 -1.11161000
H -5.12838300 -0.12504600 -0.47694700
H -5.75246200 -0.22022600 1.93411400
H -3.98663500 -0.10382500 3.67800000
H -0.51723700 0.14856600 2.81931600
B 10 29 F
B 17 29 F
D 4 7 8 9 F
Using TD-DFT I got results that made chemical sense for a T1 state. Any help would be much appreciated
The image labeled T1 is from TD-DFT
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Dear Peyton, did you make sure that you are always comparing the same T1 excited state?
I've modeled UDFT and TDDFT excited states, both open singlet and triplet, and I've obtained quite similar results. However, changing the geometry also changes the distribution of the excited states, such that T1 may have been moved to other Tn position.
Best, Pablo
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I have several cell in one picture, all of them contains vesicles. The cells labelled with red marker. the vesicles labelled with green marker. I would like to know each cell how many vesicles located inside. I would like to count all cells which are located in my image at once. How it is possible to do it?
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Depending on which tool you use, there are image processing packs that offer you several possibilities as far as image segmentation goes. MATLAB, C++, Python and other environments/languages allow you to customize your algorithm/program.
You will also need to work with pattern recognition techniques for clustering and classification of your subimages.
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I am using a fluorescence microscope with DAPI filter, here are the specifications:
Excitation wavelength: 360/40 nm
Emission wavelength: 460/50 nm
Dichroic mirror wavelength: 400 nm
I want to label my cells with cyan fluorescent protein. I just want to know if our DAPI filter can detect the CFP. Thanks.
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May consider using Permai fluorescence dye.
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Which fluorescence markers are best to use for staining macrophages. I want to prepare sample to get training with the microscope for my research.
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Thank you very much . really grateful to you.
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I would like to know if you have any guide or research work to configure the parametric label. I have searched for information but I have not found almost nothing about it except the technical notes of the program.
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  1. Define material properties and section properties then Assign frame sections to structural elements then Define frame hinge properties as "Parametric - PMM."then Specify axial force (P), moment about local 2-axis (M2), and moment about local 3-axis (M3) then Assign hinges to relevant structural elementsthen then Analyze the model and review resultsthen then Adjust hinge parameters if necessity then Document configurations for reference.
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Greetings
Is it possible to label plant probes with biotin for Oligo-FISH in the lab, or is it effective to use such labeled probes commercially?
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Labeling plant probes with biotin is a common technique used in molecular biology research. Biotin-labeled probes are advantageous because they allow for nonradioactive detection and are safer to handle than radiolabeled probes. Here’s how you can label plant probes with biotin:
  1. Design Your Probe:First, design a short DNA or RNA sequence that specifically targets the region of interest in your plant sample. This probe will hybridize with complementary sequences during the Oligo-FISH experiment.
  2. Synthesize the Biotin-Labeled Probe:You can either synthesize the probe in-house or purchase a commercially available biotin-labeled probe. To synthesize it in-house:Obtain a biotin-labeled nucleotide (such as biotin-dUTP or biotin-UTP). Incorporate the biotin-labeled nucleotide into your probe during the synthesis process (e.g., PCR amplification or in vitro transcription). Purify the labeled probe to remove any unincorporated nucleotides. If using a commercial probe:Purchase a pre-labeled probe designed for your specific target.
  3. Hybridization and Detection: Perform the Oligo-FISH experiment using your biotin-labeled probe:Fix and permeabilize your plant cells or tissues. Hybridize the labeled probe to the complementary RNA or DNA in your sample. Wash away unbound probes. Detect the bound probes using streptavidin-conjugated enzymes (such as alkaline phosphatase) that specifically bind to biotin. Add a substrate (e.g., NBT/BCIP) to visualize the signal.
  4. Advantages of Biotin-Labeled Probes:Nonradioactive: Biotin-labeled probes do not involve radioisotopes, making them safer to work with. High Affinity: Biotin has a strong binding affinity for streptavidin, enhancing detection sensitivity. Cost-Effective: Biotin-labeled probes are generally less expensive to generate than isotopic variants. Long-Term Stability: Biotin-labeled probes are easier to store for extended periods.
Remember that the effectiveness of your biotin-labeled probe depends on its specificity, concentration, and hybridization conditions. Proper controls and optimization are essential for successful Oligo-FISH experiments. Happy probing! 🌱🔬
For more detailed protocols, you can refer to the SpringerLink article on Nonradioactive Plant Small RNA Detection Using Biotin-Labeled Probes1.
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While making forest plot Revman I label the plot as Left control and right experiment. I see that the data supports the control as the diamond is on the left. But when I change labels the diamond stays in the left and is now in favor of the experiment. How to fix that? The data should be in favor of control regardless of what side of the plot the labelling is right?
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In RevMan 5.4, which is primarily used for conducting systematic reviews and meta-analyses, you typically do not directly create forest plots within the software. Instead, forest plots are generated using statistical software packages such as R, Stata, or Review Manager, and then imported into RevMan for inclusion in your review.
Here's a general outline of how you can label forest plots in RevMan 5.4 once they have been generated:
  1. Generate Forest Plot: Use statistical software (e.g., R, Stata, Review Manager) to conduct your meta-analysis and generate the forest plot according to your analysis plan.
  2. Export Forest Plot: Save the forest plot as an image file (e.g., .png, .jpeg) or a vector graphic file (e.g., .svg, .pdf) from the statistical software.
  3. Import into RevMan: In RevMan, navigate to the section of your review where you want to insert the forest plot. Click on the "Insert" menu and select "Image" to import the forest plot file you saved earlier.
  4. Labeling: Once the forest plot is inserted into your review, you can add labels or annotations directly within RevMan. To do this, click on the forest plot image to select it, then use the text box tool from the toolbar to add labels, arrows, or other annotations as needed. You can also adjust the font size, color, and style to make the labels clear and easy to read.
  5. Save Changes: After labeling the forest plot, make sure to save your changes in RevMan.
Remember to follow any specific formatting guidelines or instructions provided by the journal or organization you are submitting your systematic review to. This ensures that your forest plots and other figures meet the required standards for publication.
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This is a first run in our lab and there are several variables that each add several days to the protocol. I will be using the DeepLabel Antibody Staining Kit from LogosBio to label c-Fos. Questions are:
Which primary antibody do you use? At what concentration? How long do you incubate?
What concentration of secondary antibody do you use? How long is this incubation?
If anyone would share experience/protocol that would be great! We have been clearing and imaging brain and other peripheral tissues as well so if we can be of any assistance, please feel free to contact me.
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In their latest publication in STAR protocols (PMID 38280198), Refaeli et al. published a detailed protocol for marking engram cells using a modified version of CLARITY in combination with a rabbit monoclonal c-Fos antibody (Synaptic Systems #226 008 [https://www.sysy.com/product/226008]).
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Hello all,
I have a question. I am trying to test my DNA probes (that are biotin labelled) that we have in our lab to perform FISH. Probes are around 80 to 200 bp. I am doing a dot blot, to test our probes. However, I can not see anything when I develop the blot with ECL.
Does anyone have a good protocol that can help me? I feel like, I am missing a fundamental point. It doesn't make sense there is absolutely nothing on the membrane, complete white.
Looking forward to hear ideas,
Dilan
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As membrane I used nitrocellulose and I denatured the nick-translated DNA probes at 95°C for 5 minutes before spotting them on the nitrocellulose, then I baked it for about 1 hr at 80°C in a drying oven. Hope this helps.
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I seeded 5e6 and 3e6 SW756 cells in two 15cm dishes with equal amounts of media on Thursday, anticipating one to be confluent Monday/Tuesday, and the other to be confluent sometime later, maybe Thursday. This is my first time testing with these, so it was just a rough guess.
However, on Monday, both were equal confluency - I could not have told a difference in plates if they weren't labelled. Is this normal? Should I give less media to the 3e6 plate to get the anticipated effect, or just plate less cells next time?
Just overall curious what factors play a role in cell growth, as the 3e6 plate grew at a much faster rate with the same conditions - aside from having a higher proportion of fresh media.
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Hello Ethan,
As per the information provided by you, there could be two possiblities.
1. You may have wrongly counted the cells while plating, or
2. You may have wrongly concluded the confluency state of cells.
I would infer that 15cm dish with 5e6 seeding density may have become overconfluent on Monday because of which there is lack of space for cells to grow further as well as the media may have been depleted of nutrients.
On the other hand, the 15cm dish with 3e6 seeding density may have almost reached confluency with some space left for cells to grow besides the media still being fresh and nutrient- rich.
I would suggest you plate less cells this thursday (say 3e6 so that it reaches 80-90% confluency after 4 days i.e., Monday/Tuesday and 1.5e6 cells so that it reaches near confluency at one/two days later i.e., Wednesday/Thursday) with the SAME amount of media in both the 15cm dishes.
Best.
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Hello,
I am using Pophelper in R to run the algorithm implemented in CLUMPP for label switching and to create the barplots for the different K (instead of DISTRUCT).
I am getting a warning message when I merge all the runs from the same K using the function mergeQ() from the package which is slightly bothering me. Can anyone help me with this?
The warning message is as follows...
In xtfrm.data.frame(x) : cannot xtfrm data frames
Thanks,
Giulia
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Have you found a solution already?
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Hi there, I used thermo's TMT 10plex kit to label peptides and pool them together for LCMS/MS analysis. I am analyzing my data in Proteome Discoverer 2.3 and wondering how to quantify the number of peptides that were labeled vs unlabeled to make sure my labeling strategy worked.
When I run the processing and consensus workflows recommended by thermo for this data, I get protein IDs and quantitation for most of the proteins- I have the TMT label set as a static modification for N-terminus for the peptides and on lysine residues. So, all of my identified peptides and proteins have the TMT label identified as a modification. Surely there are some peptides that the program can identify that weren't labeled, so how do I find those peptides and determine the ratio of labeled:unlabeled peptides in my sample?
Also, even though all of the identified peptides in this workflow are marked as having the TMT label, not all of them are quantified. Does anyone know why this happens?
Thank you!
Talia
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Hi Talia,
I have looked into this particular question in the Methods paper I have published ; please see the link below. I did not use Proteome discoverer - I used MaxQuant and configured each of the TMT channel modification as a variable modification so that I could search my mass spec file to see how many peptides were not labeled.
Let me know if you can access the paper link below, if not I can also just email it to you.
All the best,
Hediye.
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I kindly notify that ResearchGate must consider the difference among the author's first names written only with one capital letter. It is obvious that published articles labeled only with surnames, are very confusing about differentiating their authors. Because of that the SCI is inevitable for scientific platform, a new approach is needed to label the articles also with the first names.
Technical scientist: SELÇUK SOYUPAK, Department of Civil Engineering, Faculty of Engineering, KTO Karatay University, Konya,Türkiye
Medical scientist: SÜREYYA SOYUPAK, Çukurova University, Medical School, Department of Pediatric Hematology-Oncology, Adana, Türkiye
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It is an insufficient way to label any published article with a surname only because:
1. A surname is a generic name, including other names allows for specificity, and;
2. A lone use of a surname may be misleading.
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Let S_2(n,k) denote the 2-associated Stirling number of the second kind for n objects and k blocks, with n being at least two. That is, we partition n labeled objects into k unlabeled blocks such that each block has at least two objects in it, and S_2(n,k) is the number of those partitions. I’d like to ask if the following is known: q(n,k) is strictly decreasing in k, where q(n,k):=S_2(n,k)/S(n,k), and S is the Stirling number of the second kind. Many thanks for the help!
Janos
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Yes.
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Supervised Learning
In supervised learning, the dataset is labeled, meaning each input has an associated output or target variable. For instance, if you're working on a classification problem to predict whether an email is spam or not, each email in the dataset would be labeled as either spam or not spam. Algorithms in supervised learning are trained using this labeled data. They learn the relationship between the input variables and the output by being guided or supervised by this known information. The ultimate goal is to develop a model that can accurately map inputs to outputs by learning from the labeled dataset. Common tasks include classification, regression, and ranking.
Unsupervised Learning
Unsupervised learning deals with unlabeled data, where the information does not have corresponding output labels. There's no specific target variable for the algorithm to predict. Algorithms in unsupervised learning aim to find patterns, structures, or relationships within the data without explicit guidance. For instance, clustering algorithms group similar data points together based on some similarity or distance measure. The primary goal is to explore and extract insights from the data, uncover hidden structures, detect anomalies, or reduce the dimensionality of the dataset without any predefined outcomes. Supervised learning uses labeled data with known outcomes to train models for prediction or classification tasks, while unsupervised learning works with unlabeled data to explore and discover inherent patterns or structures within the dataset without explicit guidance on the expected output. Both have distinct applications and are used in different scenarios based on the nature of the dataset and the desired outcomes.
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In the realm of machine learning, the main distinction between supervised and unsupervised learning lies in the nature of the dataset used for training.
Supervised Learning Dataset:
In supervised learning, the dataset consists of labeled examples, where each data instance is associated with a corresponding target or output value. The dataset includes both input features and the desired output or target variable. The aim of supervised learning is to learn a mapping function that can accurately predict the target variable based on the input features. The model is trained using labeled examples, allowing it to generalize and make predictions on unseen data. Common examples of supervised learning algorithms include linear regression, decision trees, and support vector machines.
Unsupervised Learning Dataset:
On the other hand, unsupervised learning involves unlabeled datasets, meaning they do not have corresponding target values. In this scenario, the model learns patterns, structures, or relationships within the data based solely on the input features. The objective of unsupervised learning is to discover inherent patterns or groupings within the data without prior knowledge of the desired output. Common unsupervised learning algorithms include clustering algorithms such as k-means clustering and dimensionality reduction techniques like principal component analysis (PCA).
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Can I use flow to compare flourescently labelled intracellular structures between different cells? Can I use it to compare the same cells before and after treatment? Is the mean intensity of a fluorescent signal a reasonable measure for relative quantification?
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I think it would be OK to compare the fluorescence intensities before and after treatment of the same type of cell, but it would be more difficult to compare different cell types because the cell types could differ in important parameters such as size, volume, and internal composition.
Is the mean intensity of a fluorescent signal a reasonable measure for relative quantification? Yes, but you should also show the distributions, since a heterogeneous distribution could lead to misleading results if only the mean were presented.
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Can i cluster documents to label them as a first step. Then in the second step, can I use the labelled documents to apply a classification method such as svm, knn, etc.?
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Classification and clustering are two methods of pattern identification used in machine learning. Although both techniques have certain similarities, the difference lies in the fact that classification uses predefined classes in which objects are assigned, while clustering identifies similarities between objects, which it groups according to those characteristics in common and which differentiate them from other groups of objects. These groups are known as "clusters".
Regards,
Shafagat
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I want to digest sumo/His label when the protein in still hang on the Ni column. I wash the undesired proteins as usual, then balance the column with 3ml enzyme digestion buffer, then add sumo enzyme for digesting the fusion protein at 4℃ for 12h in 4ml sumo digesting buffer. Then catch the flow through liquid, take some sample from flow through liquid and beads after elution for SDS-PAGE, the brand explains the label did not have been cut, it is still on the beads entirely.
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G.Dharmamoorthy First of all, I really appreciate your help. About changing buffer. I already balance the column with sumo cleavage buffer before adding the sumo protease. Unfortunately is still not working.
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We have to add specific molar concentration to the media (10^-6M). it is labelled as 1000X how can we convert it.
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If it is labeled as 1000X, it is intended to be added at 1 part per 1000 by volume. There is no way to convert to molarity unless the concentration in molarity or g/L is stated on the package or in the product information.
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Im looking into brand marketing for underground record labels and also marketing for releases such as singles and EPs. I would like to find studies on the most effective methods of marketing within the music industry.
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The music industry employs a variety of marketing techniques to promote artists and their music. Social media plays a pivotal role, enabling artists to connect with their fans directly and share content. Influencer collaborations, where musicians partner with popular online personalities, amplify their reach. Streaming platforms offer personalized playlists, and artists can utilize data-driven insights for targeted advertising. Music videos on YouTube and Vevo create visual engagement. Live performances and tours not only generate revenue but also build a loyal fan base. Partnerships with brands and sync licensing in commercials or movies broaden exposure. Lastly, viral challenges and trends on platforms like TikTok can propel a song to stardom.
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Might be a stupid question but I can’t find papers about it: can fluorophores (rdTomato, EGFP etc) be taken up by EVs and transported, or do they only get attached to the surface? All I could find was when researchers are attaching it on purpose to the surface to label EVs. However, I want to know whether a fluorescent cell would produce EVs with that fluorophore inside?
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Just saw that I wrote RdTomato, i meant TdTomato of course
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I am planning to perform C13 MFA, and I was wondering how I can weigh the labeled glucose and add it to the media while maintaining sterility. I didn't find any protocol specifically explain this part.
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Assuming that the labeled glucose powder is not sterile, you have 3 options.
1. Prepare a concentrated stock solution of the labeled glucose and pass it through a sterile syringe filter into a sterile container.
2. Prepare the medium from powder, including the labeled glucose powder, and then sterilize it in the usual way.
3. Add the non-sterile labeled glucose as powder or stock solution directly to sterile medium, then re-sterilize the medium by membrane filtration.
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How do I get a superscript in my axis labelling in Minitab? I need my axis to read mg g-1 but not mg/g. Any suggestions?
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Try holding ALT key and type 0179
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Dear all,
I'm trying to perform expansion microscopy, which work quite nicely, but we have this recurring problem of cells cracking.
Here is the protocol I'm using:
After labelling, incubation in acryloyl-x SE 0.1mg/mL O/N at 4°C
Polymerization in a monomeric solution containing 0.4% TEMED and 0.2% APS for 1h at 37°C
Denaturation in a buffer of 50mM Tris, 200mM NaCl and 200mM SDS for 1h30 at 95°C
Gels back in PBS for 30min then labelling with DAPI for 15min.
Finally expansion in water (changed twice) O/N.
We have an expansion factor of ~3-4, which is fine for us, but most of the cells are fragmented/torn in their cytosol (the cell itself, not the labelling which is very nice)- never the nucleus.
My hypotheses are either the denaturation is not good enough or the expansion is too quick.
Does anyone had the same problem and could give me a lead?
Thank you very much in advance!
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I have the same problem, have you found the reason yet?
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Good evening,
Can you please advice on a protocol for labeling of OMVs for cellular uptake assay. OMVs are derived from gram-negative strain. I was thinking of applying R-18 but other dyes are also possible. The readout is spectrophotometer. Do I need to wash and/or lysed cells before measuring fluorescence? Thank you for you help.
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PBS is a a reasonable choice because it lacks amino groups that could react with the dye. However, it is usually recommended to use fresh 0.1 M sodium bicarbonate which, being a bit more basic than PBS, increases the rate of the labeling reaction.
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After adding the tree block to my NEXUS file, I get the "Error parsing sequence data; Tree labels do not match alignment labels" error message. I've checked the labels multiple times, they're all correct, no additional tabs or spaces. The tree itself also looks correct. Could there be something else leading to this error? The NEXUS file wihtout the tree block works well.
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Isabell Hummel The error message you've encountered in PopART, which reads "Error parsing sequence data; Tree labels do not match alignment labels," indicates a mismatch between the labels in the tree block and the labels in the alignment block within your NEXUS file. Despite your efforts to verify the labels, it's important to consider additional factors that might be contributing to this error:
1. Case Sensitivity: Confirm that the labels in both the tree block and alignment block match in terms of capitalization. NEXUS format is case-sensitive, so even minor differences in capitalization can cause this error.
2. Label Consistency: Double-check for any labels in the tree block that may be missing in the alignment block or vice versa. Even a single missing or additional label can lead to this error.
3. Whitespace Characters: While you mentioned verifying tabs and spaces, it's advisable to also inspect for any non-visible whitespace characters, particularly at the end of lines or within the labels, as these can sometimes be the source of the issue.
4. Special Characters: Ensure that the labels do not contain any special characters or symbols that might not be recognized correctly by the software.
5. Tree Format: Validate that the tree block adheres to the correct formatting specified by the NEXUS format standards. Ensure that it starts with "TREE" and that the labels are enclosed correctly.
6. Label Length: Some software may impose limits on label length. Examine your labels to ensure they are not excessively long, which could exceed character limits in PopART.
If you've meticulously reviewed these aspects and the error persists, consider recreating the tree block or alignment block from scratch. Occasionally, unnoticed errors can slip into the file during editing. Additionally, verify that you are using the most up-to-date version of PopART, as software updates may address compatibility issues.
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Hello everyone! I am studying Graph Neural Networks to apply to my field.
My problem: I have a dataset with multiple graphs. Each node in a graph have Y label. I want to predict Y label of nodes in new graph.
I want to ask: I can make predictions by Graph Neural Network? If can, Could you give me some hints?
Below is a illustration about my question.
Thank you!
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It seems your problem is to find isomorphic subgraphs. The search can be accelerated by assigning statistical signatures to the vertices and edges. An example assign the degree of a vertex and the sum of degrees of this vertex's direct neighbors.
Maybe of interest
Regards,
Joachim
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Dear colleagues:
I have used the Vybrant™ Alexa Fluor™ 555 Lipid Raft Labeling Kit on the HT22 cell line. I followed the indicated protocol, followed by 4% PFA fixation (20 min), phalloidine staining and mounting with Vectashield. Then, I acquired some pictures by using confocal microscopy (40X oil objective, 2X digital zoom) and found that not every cell got the stain. I have seen some publications that used this kit in other cells, and they show that every cell get the stainning (at least they show that). Does anibody experienced something similar using this kit??? what could be the pissible reasons to explain this results????
Best,
Felipe
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  • The HT22 cell line may not have as many or as prominent lipid rafts as other cell lines, leading to inconsistent staining results. The kit might work optimally with specific cell lines or conditions and less effectively with others.
  • Cells that are too densely packed may not take up the stain evenly, resulting in variation between cells.
  • The fixation and permeabilization steps are critical for successful staining. If these steps are not performed correctly or if the conditions are not optimized for HT22 cells, it can lead to incomplete staining.
  • The duration of incubation with the labeling reagents can influence staining results.
  • The health and viability of the cells can impact staining results. Cells that are stressed, dying, or not in an optimal state may not take up the stain as efficiently as healthy cells.
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Computer vision tasks, such as object detection, have traditionally relied on labeled image datasets for training. However, this approach is limited to detecting only the set of classes present in the training data. Zero-shot object detection (ZSD) is a breakthrough in computer vision that allows models to detect objects in images based on free-text queries, without the need for fine-tuning on labeled datasets
This capability has significant implications for businesses, as it enables more flexible and adaptable computer vision systems. In this blog post, we will explore how zero-shot object detection is changing computer vision tasks in business and discuss some of the key benefits and challenges associated with this technology.
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Zero-shot object detection (ZSD) is the task of object detection where no labeled training data is available for some target object classes. For example, there are times we'd like to detect objects that are hard to find in the class list of pre-trained models.
Regards,
Shafagat
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Hellow Everyone .
I have a query that i have build a monomer of both phospholipids and glycolipids for Md Simulation but the problem which i face is that once i go to tool/software and put the molecules click on label atom than their numbering come on randomly can't come on proper manner than how to solve this issue thanks in advance
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Thanks
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For the English version of the Pure Procrastination Scale (Steel, 2010) the scale is listed as having a 5 point scale ranging from 1 very seldom or not true of me to 5 very true of me. However, what are the middle anchor point labels for the scale? Thanks
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1 very rarely or not applies to me
2 rarely applies to me
3 sometimes applies to me
4 often applies to me
5 very often applies to me
It was easier for me to translate from German
see also
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I and a second coder coded data coming from open-ended questions from a survey, and now I want to calculate the intercoder reliability.
I want to use the KALPHA MACRO to calculate Krippendorff’s α, but I am not sure how to do it with data like mine, where each data unit could be coded for more than one categorical/nominal code.
All the examples I find work with binary nominal coding.
Does anyone can help me with this?
Thank you in advance
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This is one of the best resources I have ever seen on the subject:
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Hello, I'm trying to find pre labelled 90mm petri dishes to be included in an automation workflow for a new biotech lab. Does anyone know a brand? I can't find one! THX
Nidia
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Binyamin Kerman very kind for your answer, thanks. Exploring all your suggestions!
N
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I extracted the genomic DNA from Salmonella Typhi culture for its molecular identification. Then designed the primers for the 16S rRNA gene, perfomred the PCR and then run the amplified product on GE along with DNA ladder. After the run, I got this picture from GEL Doc system.
But unfortunatetly I lost the DNA ladder specifications, and now unable to label the amplified gene bp size as well as the DNA ladder size and labelling.
You are professionally request to sort this out?
Thanks in advance.
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I hope/guess you know, which DNA ladder from which company you used. So simply look at the product specifications on the website or contact the provider. If you don't know, which ladder you used there is no way to properly label the size of your products and the marker. Next time document all experimental details in your lab-book including the marker specifications.
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Hi,
I am trying to stain different cellular compartments in bone marrow sections from adult mice. When I use direct labelled antibodies, for example CD3-FITC, CD9-PE etc, the staining result is always very bad. I can not see any specific signal. But when I try with primary and secondary antibody, I usually get good result. Curretly there is an experiment that requires to use direct label antibodies. Therefore, I am wondering if there is any good way for me to stain bone marrow sections using direct labelled abtibodies. Thanks.
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hematopoietic tissues using . please follow science direct bone marrow topics
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I'm trying to predict a disease based on both facial features and questionnaires.But I have two different datasets such that one is on the question and answers with labelling and the other is with images and labels as well. I mean both the datasets are different but they predict the same thing. How can I merge and use these datasets together to predict using deep learning?