Running - Science topic

Eugenio Rhoads

You're correct that using models like the Freundlich and Langmuir equations to estimate soil capacity for phosphorus (P) fixation can be accurate but may be complex for farmers to implement directly. Fortunately, there are more practical and farmer-friendly approaches available:

1. Soil Testing: Regular soil testing is a simple and effective way for farmers to assess soil fertility, including nutrient availability and fixation potential. Soil testing laboratories can provide farmers with comprehensive reports that include recommendations for nutrient management based on the specific characteristics of their soils.

2. On-Farm Trials: Conducting on-farm trials allows farmers to observe the performance of different management practices, including fertilization strategies, on their own fields. By comparing the results of different treatments, farmers can gain practical insights into how their soils respond to different inputs and make informed decisions about nutrient management.

3. Soil Amendments: Applying soil amendments such as lime or gypsum can help reduce phosphorus fixation in soils with high levels of aluminum or iron oxides. These amendments can help improve soil pH and cation exchange capacity, making phosphorus more available to plants.

4. Precision Agriculture Technologies: Advancements in precision agriculture technologies, such as remote sensing, soil mapping, and variable rate application, enable farmers to manage their fields more efficiently and effectively. By accurately targeting inputs based on site-specific soil and crop conditions, farmers can optimize nutrient use and minimize the risk of phosphorus fixation.

5. Integrated Nutrient Management: Adopting an integrated approach to nutrient management that combines organic and inorganic sources of nutrients can help reduce phosphorus fixation and improve soil fertility over the long term. Practices such as crop rotation, cover cropping, and organic matter addition can enhance soil health and nutrient cycling, reducing the need for synthetic fertilizers.

By combining these practical approaches with scientific knowledge and expertise, farmers can effectively manage phosphorus fixation and optimize nutrient use in their agricultural systems. Agricultural extension services and agronomic advisors can also play a valuable role in supporting farmers in implementing best management practices tailored to their specific circumstances.

Pleun St Write exactly what you wrote to install it?

Thanks for your answer

It's difficult because of the presence of substances that interfere with protein assays (detergent, reducing agent, dye). The approach I would take would be to run the samples on SDS-PAGE, transfer the gel to the Western membrane, stain it temporarily with Ponceau S, and photograph the stain in white light with a gel documentation instrument. Then you can integrate the density of all the bands in each lane as if they were a single band. Then you can destain the membrane and use it for immunoblotting.

Hongye Qin Yes Exactly! Well thank you :)

I'll surely get back to you.

did you find any solution? Kindly share. I am also facing same problem

Hi Sean,

Curious if you came up with a way of automating this in the end? Let me know if you did!

Thanks

Hi Chelsea St. Germain,

Did you overcome this issue? I also got this problem even with the files that were previously successfully analyzed.

Best,

Alex

It was the commercial 18.2 that had the error. Thanks

While the exact specifications of the iPhone 15 Pro aren't officially available yet (as of May 7, 2024), it's likely that its 6GB RAM will be sufficient to run MATLAB's animated or dynamic 2D plots. Here's why:

However, there are some caveats to consider:

Overall, for basic animated or dynamic 2D plots, the iPhone 15 Pro's 6GB RAM should be sufficient. But for more demanding tasks, consider using the desktop version of MATLAB on a computer.

Here are some additional points to keep in mind:

I recommend checking the official documentation or contacting MathWorks (MATLAB's developer) for specific information on MATLAB Mobile's capabilities and compatibility with the iPhone 15 Pro.

Lack of fit F test is used to test the hypothesis of model correctness: that is whether there is statistically significant model misspecification in not.

In RSM, the Lack of fit test is used to determine whether there is significant curvature in the experimental data: the first order model with interaction term is not able to capture adequate data variation and hence a second order model based design (CCD or Box Behnken type) is needed to fit the data coming from this particular region of the experimental space.

Details about the RSM process and statistical tests associated with it can be found in the following books:

1) Chemometrics: Experimental Design by Ed Morgan

2) Response Surface Methodology by Myers, Montogomery and Cook

Paul Rutland Thank you!

You are welcome sir

I'm available for any collaborative workflow

In order to have a similar retention time increase the flow rate by a factor of 3.5

You can increase the load by a factor of 3.

Hey there Abdulwasiu Salaudeen Olawale!

MetaboAnalyst 6.0, huh? That's a solid choice for your untargeted metabolomics studies on soil and plants. I've used it before, so I can definitely give you Abdulwasiu Salaudeen Olawale a hand.

First off, if you're looking for a tutorial video, I suggest checking out the official MetaboAnalyst YouTube channel. They've got some great resources that can walk you Abdulwasiu Salaudeen Olawale through the process step by step. Also, the MetaboAnalyst website itself has detailed documentation that's worth a read.

As for running it on your Abdulwasiu Salaudeen Olawale LCMS/GCMS data, let's break it down:

1. **Data Preparation**: Make sure your Abdulwasiu Salaudeen Olawale data is in the right format. MetaboAnalyst usually prefers data in common formats like CSV, Excel, or TXT.

2. **Upload Your Data**: Head over to the MetaboAnalyst website, sign in, and upload your Abdulwasiu Salaudeen Olawale data. Follow the prompts to select the appropriate analysis type and upload your LCMS/GCMS data.

3. **Parameter Settings**: Set your Abdulwasiu Salaudeen Olawale parameters according to your study design and the type of analysis you're doing. This includes things like normalization, scaling, and statistical tests.

4. **Analysis and Interpretation**: Once your data is uploaded and parameters set, run the analysis. MetaboAnalyst will crunch the numbers and generate results for you Abdulwasiu Salaudeen Olawale. Take some time to interpret the results and see what insights you can gather.

5. **Collaboration and Publishing**: Absolutely, collaboration sounds like a great idea! Once we've got some solid findings, we can definitely work on publishing together. It's all about spreading knowledge and advancing science, right?

Feel free to reach out if you Abdulwasiu Salaudeen Olawale need any specific help with any step of the process. I'm here to make sure your metabolomics study goes smoothly!

Thanks for the answer.

We tried using 5% methanol in the dosing buffer, but there was no improvement.

Yes and no. There are minor differences which can cause your results to be false. Reducing the grid box would definitely decrease the total volume so that the interactions will increase just like in a more concentrated situation. However, when your ligand and/or receptor (target molecule) goes outside the walls of grid box, it comes back from the other side (Leaving the grid from right, entering from left etc...). So in that sense, you create a new collision probability for the atoms to interact. Let's assume you have a tiny drug that you want to bind to your large protein to inhibit/suppress its activity. In terms of theoretical chemistry, it may not have any chance to attack and residue on the right side of that protein. As you limit the grid-box, some right parts of that protein will enter from the left side and your drug will be able to interact that side as well which is not supposed to happen. So if you want to increase the concentration go ahead and do it. Please do not decrease the grid-box volume to satisfy that need since it will yield wrong outcomes.

The problem was with the Human Brain Whole Tissue Lysate (Adult Whole Normal), novus. When compared with colorectal cell lysate, this difference was obvious. Thank you all for your comments.

Could it be that your cold trap packing material is moving around and sometimes creating channels? Sometimes it is packed firmly and sometimes it is loose.

Dear Sree Haryini ,

Here is suggestion from me:

1. Reduce the number of frames: As you suggested, using a smaller portion of the trajectory can significantly reduce computation time. However, make sure that the selected portion is representative of the system's behavior.

2. Increase the interval: Instead of analyzing every frame, you can increase the interval between frames. For example, you can analyze every 10th or 20th frame instead of every 5th frame. This will reduce the number of calculations needed.

3. Parallelize the computation: Calculate by using multiple core, you can parallelize the computation to speed up the analysis. Many MMPBSA tools support parallel computing. If you use the tool by Valdes you can add mpirun -np X and X can be replace with number of processors. Example mpirun -np 16. But you also need to consider the RAM available.

4. Calculate MMGBSA - As suggested by Martin Rosellen calculate GBSA is indeed will reduce the workload for calculation. I have try before that calculate the GBSA take 2 hours while PBSA take almost 1 days in the same trajectory.

I know it's been a long time since you asked and you've probably solved the problem. But I'm experiencing a similar problem and have the same difficulty.

It seems that in your code you are using global arrays when referring to pointers, as in.

pt21 = SMAFloatArrayCreate(21,101,0.0)

Although they are global in the sense that all threads have access to them, they do not guarantee thread safety. From what I read in the documentation, you would need to use mutex to lock the threads (losing performance).

Or simply define it as a local array (in the sense that it's local to a thread and doesn't need any locking for access, therefore thread-safe). Something like this:

pt21 = SMALocalFloatArrayCreate(21,101,0.0)

And they return with

... = SMALocalIntArrayAccess(ID)

If you managed to solve the problem, please give me feedback on how you did it. I'm facing the same problem and I can't compile my subroutines on demand.

In CFA and SEM, the default estimator in most software programs is maximum likelihood (ML). ML estimation is based on the assumption of multivariate normality.

Ignoring multivariate non-normality and using regular (uncorrected) ML estimation can lead to biased tests of model fit (e.g., chi-square), biased standard errors, and incorrect tests of statistical significance. The parameter estimates (e.g., factor loadings, regression and path coefficients) are relatively unaffected by non-normality.

If your data are non-normal, rather than applying data transformations, you can simply use robust ML estimation (e.g., Satorra-Bentler correction; Bollen-Stine bootstrap) to obtain corrected standard errors and test statistics. Alternatively, you can use an estimation method that does not require normality (e.g., weighted least squares or WLS estimation). However, WLS requires very large samples to provide valid results, so most researchers choose use the first option (robust ML estimation).

You can also check out my Youtube videos on this topic here:

you do appear tp have weakly visible bands in this image so I would check that your sample loading dye is dense enough to hold the sample in the well. If so then run 3 or 4 more cycles of pct to get more product. You mention that the gel is not covered with running buffer. It is usual to cover the gel completely with running buffer to get good results. You could just load more sample and the gel would look better in this case but more cycles would be better

Well, sure. It could have been avoided. Most are engineers in this thread (not me, full disclosure), so you know--we can build anything to manage any flows produced by any event you'll likely encounter (at least for some period of time). But, of course, the question is, should we? It's always a balance between stormwater management infrastructure lifespan versus the chance of seeing a large, rare event when you decide what to build and decide what to spend on such facilities. At this stage, as the climate continues to adjust to excessive GHGs, my advice is to buy the land one needs for future expansion of stormwater facilities, but build infrastructure now that is sized more closely to today's expected flows. It's possible all of this is happening in the UAE now, but I know nothing about their programs and have no time today to head down a google rabbit hole to learn them. Perhaps others can chime in on what goes on there. Cheers!

First, you need to do the correlation between your target variable and each of your potential independent variables. After checking what independent variables are the more correlated to your target variable (as mentioned earlier coefficient correlation closest to - 1 or + 1). Once, you decide according to these correlation coefficients which variables you can select for your model, you need to ensure that there will be no multicollinearity in your model. To ensure that, for each independent variable you do correlation tests again. If two independent variables are too correlated, you should introduce only one in your model (e.g. the variable which had the higher correlation rate with your dependent variable).

I also met this question, I dont know what to do

@Pedro Weiss Mattioli

Hah, interesting. I am surprised that there is still people can find my question post even at this time. I am pretty thanks for your sharing and suggestion. For what your mentioned, that's correct. Capacity of your RAM or storage space can be the reasons account for the error, and I actually confirmed that. But enven without these issues, this error can still happened sometime. According to what I heared, similar question can also appear in other computational mechanics field. Weire, huh? Anywhy, considering it is a problem not too much to appear, I do not troubled by it anymore currently.

Thank you.

Robert Matthews is it possible to ask for a personal code like:

1. First letter of your mother's first name

2. Last letter of your mother's birth name

2. Second letter of your father's first name

3. First letter of your birth place

4. Day of your birth

For example RROG12

This ensures (almost) that every participant will have a unique code, which can easily be reconstructed at several time points.

Joshua Michael Staley how did you take into account that Robert is probably not able to match the pairs, therfore, he will not be able to calculate diference scores?

thank you so much

1.The numpy.linalg.LinAlgError is an exception that occurs when you're performing linear algebra operations using the NumPy library, and a mathematical condition prevents the correct execution of the operation. This error usually happens when you attempt to invert a singular matrix (a matrix that has a determinant of zero) or perform an operation that relies on matrix inversion, such as solving a system of linear equations or computing the inverse of a matrix.

2.use other versions of python for run this code , or search the best version of python for packages used in your .py file.

- Also don't forget to update the -apt.

With respect to ethical principles, conventional banking and Islamic banking are not a good match.

Source of the synopsis:

Did you code the grouping variable wrongly? It says that one group has a sample size of N = 1. If it worked before, I would guess that you recoded something, maybe by accident. Check your raw data, for example: get the frequencies of every category in your grouping variable. Does that correspond to the sample sizes for each category that you gathered?

Otherwise show your data here on RG.

Specimen management in microbiology plays a critical role in ensuring the accuracy and reliability of laboratory test results. Here are some significant effects of specimen management on microbiology laboratory tests:such as Contamination Prevention: Proper collection, handling, and transport of specimens help prevent contamination, which can lead to false-positive results or misinterpretation of test results. Contaminated specimens can introduce foreign microorganisms into the culture, interfering with the growth and identification of target pathogens.and Viability of Microorganisms,Preservation of Microbial Characteristics,Optimal Growth Conditions,Timeliness of Testing, Quality Control and Assurance, specimen management is a critical aspect of microbiology laboratory practice, and deviations from best practices can significantly impact the accuracy and reliability of test results.

can you share how did you solve it?

For those still interested. A nice tool to (among other things) draw a helical wheel plot is Heliquest: https://heliquest.ipmc.cnrs.fr

This user-friendly tool also enables the so-called Heliquest-based Eisenberg plot approach to distinguish between amphipathic, transmembrane helices etc., see for more details:

Thank You sir for your reply. I will look into this!

The below link can effectively help you:

See "Empirical Dispersion" in "More" tab.

Thank you for your kind comment. I am sure the fellow will move on undisturbed. Hopefully, the horrendous critique will hold other people back...:) They are a dime a dozen...:)

If you are at LinkedIn look me up. I will be presenting videos explaining the theory. I am just preparing the explanation of the Fine Structure Constant...:)

But maybe isolate genomic DNA and try PCR with Sanger sequencing or T7EI assessments rather than western since you will have a lot of wells (aka clones) to select from.

Hello, Vignesh.

A secondary phase with only one peak is quite problematic because we may not be able to identify it. But if you have a clue about it, we may try to find it.

Best regards

Ricardo Tadeu

1. A vacuum is a region of space with no matter; a super vacuum could be defined in one of two ways, depending on whether it is a concept, or a description of current technology. In the first instance, it with be a region of space with neither matter nor energy (in which case, unless an extremely small region, it does not exist, because any part of space big enough to see without a microscope would at least have light of some sort passing through it (e.g., at least the Cosmic Background Radiation). In the second instance, it could be used to describe a "laboratory" vacuum which has far less matter in it than any previously created laboratory vacuum.

2. The Earth is in a region that is essentially a vacuum, because most of the space between the planets has practically nothing in it at any given time. However, there are cosmic rays and the Solar Wind everywhere, so though merely pieces of atoms, there is some stuff everywhere in space; but the amount is so small that for all "practical" purposes, it is a vacuum.

3. Dark energy is a fiction created by cosmologists to explain why, despite having too little mass for the gravity of that mass to fight the tendency of empty space to expand (per Einstein's General Theory of Gravity), the geometry of the Observable Universe is "flat", which would require something to add up to 100% of the "critical mass" of the Universe, and since visible and unobservable ("dark") matter makes up at most 27% of the critical mass, cosmologists created the concept of dark energy to make up the remaining 73%. However, there is no need to presume that the Universe is flat. Just as the Earth is a globe but looks essentially flat (on the average, and particularly at sea) because you can't see enough of it to see its real shape, the Universe is actually what is called "hyperbolic" in shape, which is exactly what you would expect if its mass is less than the "critical" mass. However, almost all cosmologists are convinced by various characteristics of the Observable Universe that the "real" Universe is at least 1000's and perhaps 10 to the 1000's of times bigger than what we can see, what we can see is too small to see its real shape, so it just looks "flat". Since by definition we can't see anything but the "Observable" Universe, we will never be able to see the true shape of the Universe; so "dark energy" will remain a "useful" fiction for calculation purposes for the foreseeable (if not infinite) future; but I am certain that we will never figure out what it is, because it doesn't exist. (Having been both a mathematician and a professional astronomer, I can assure you that even when something like "dark energy" doesn't exist in real life, creating a mathematical model that includes it, in order to make the math work right, is considered perfectly OK by professional mathematicians.)

I think the problem is mainly due to 'uniprot_sprot.fasta' having too few overlapped sequences with Mayfly (currently, there are only 3 proteins for Rhithrogena germanica and around 500 for Rhithrogena spp. in Uniprot. ). Therefore, only a few peptides are identified, and eventually, the FDR filtering kicks in early, cutting the list of identified peptides short. I would obtain the full protein sequence of Mayfly before conducting proteomics.

That's the point: The control should have the same OD as there blank (0). How much is 0 in spectrophotometry? Does 0.001 count as contaminated? 0.010? 0.100? The purpose of putting a non-inoculated control was to check if the medium was contaminated.

Now I can run it successfully. I am writing down the answer in case if someone face same error in future. I have updated SE3nv.yml environment and reinstalled the pytorch version. So now the setup is running for every example without any issue.

Problem has been resolved!

What is the retention time for the peak in acetonitrile? What is the retention time in methanol?

"Ghost" peaks come from contaminants. Null runs without injections showing the peak tends to rule out the injector as a source of the peak. The peak seems very reproducible.

One source of ghost peaks are contaminants in solvents. It could be the formic acid or water, you still saw the peak when you tried methanol which suggests the acetonitrile might not be the source.

If you post a chromatogram, it would be helpful.

Rotary scrapers were made for this: https://www.fishersci.com/shop/products/rotary-scraper-100-250-500ml/501354676

Otherwise, if your solute is really precious and you don't want to lose any product at all, take it out just before it's dry, put it in a tray or other wide, flat container that will be easy to collect solid off of, and finish the drying in a vacuum oven. https://labovens.net/collections/vacuum-ovens

You are using categorical independent variables. How could there be a logistic relationship???

That's true. We've been pondering it, it's good to hear someone from the outside recommending it.

Thanks

Is the screw on the z axis rotating during the approach? Is the sample sufficiently flat to approach the surface with no macroscopic asperities which may interrupt or block the piezo movement?

The cut-off energy depends on the pseudopotentials, so if you're using the same pseudopotentials you should use the same cut-off energy.

The k-points sample the reciprocal-space of your simulation cell. If you make a supercell, the real-space cell is increased in size by some multiple, let's call it X. The corresponding reciprocal-space cell as been *reduced* in size by the same factor X, and since you don't have as much reciprocal space to sample you don't need as many k-points; in fact, if you used to have N_k total k-points you now need only N_k/X (although that may not be an integer, so you have to think carefully about exactly what the appropriate sampling would be).

Hope that helps,

Phil Hasnip

(CASTEP developer)

it seems that you're using an older version of DAMASK. I recommend to update to the most recent version (3.0.0-beta)

It sounds like you could have some binary DVs (use versus nonuse) which would require logistic regression.

If you already have hypotheses about the number of factors and the loading pattern (which variables measure which factors), EFA may not be needed and you can proceed straight to CFA.

Ayaz Anwar make a single pdb file for every combination and try using protein-only option.

I'm always reluctant to run a rotor at its maximal rated speed, especially if I don't know how well it has been treated in the past or how old it is. Chances are the stated time and g-force in the protocol are more than sufficient, so spinning at 40,000 g for 80 minutes will probably achieve the desired result.

Of course, if different rotor and tube geometries are used, results will vary because of differences in k-factor, as Engelbert Buxbaum mentioned. People usually don't specify all the details of the centrifugation, such as the rotor used or the type of tube, so it may not be possible to compare k-factors.

Hi Babak

Can you tell me how you created "history variables" in UMAT? I assign my hsitory value to a "statev" in UMAT but ABAQUS outputs it as a "Field Output" and not a "History Output".

Thanks

You don't need to install on your computer. The easiest option would be ColabFold.

Do you have a good solution yet, I'm having the same problem

plz download and use

Thanks so much, AbdallFatah

Thanks Freyda Lenihan-Geels Raja Singh I ran the complex in TBE buffer with a pH of 7.8, and the gels were made with a pH of 7.8. I reversed the leads. As a result, the complex has moved, but the free DNA is difficult (the bands are not crips yet but this gave me sense that complex is shifting)

Hi Shon, you're right. The 28/18 ratio is great, but the positions seem to be off. Are you sure the lather you used was not for DNA by any chance?

Plus, may I ask what was your purification method? the product is really clean.

Yes, it is possible. That kind of simulation is called "implicit solvent molecular dynamics simulation". This simulation is notably much faster than the explicit one, albeit at the expense of reliability since the simulation doesn't reflect the real biological environment, as previously mentioned by Arnab Mukherjee .

To perform this, ordinary MD simulation software like GROMACS can be used and modified accordingly. Alternatively, commercial software such as MOE can also be employed for this purpose.

Dongliang,

1: If you're not put off by kiloamp currents, I can't see a reason to be troubled by short-circuiting that component. Note the peak tolerable current - much higher and I imagine that damage to the plates or terminals could occur.

2: It depends on how much charge is in it. You know the capacitance, so you know the total charge at a given voltage. If you allow current to flow, you can write down an expression for the charge (and thus voltage) remaining.

3: The surface of what? The capacitor is a composite device - and the plates won't be thermally well-coupled to the outer skin of the device. The terminals are better connected (!) to the plates, thermally.

4: It depends on the chemistry of the electrolyte.

I usually use the Yasara-Structure application to carry out molecular dynamics simulations up to 100 ns, which only takes up to 6 days. Additionally, I use a PC with 16 cores

Hello, a bit more information would be useful to help diagnose the problem but just looking at the fronting peak shape my guess would be a sample solvent that has an eluotropic strength that is too high combined with an injection volume that is also too high. Try injecting a lower volume or matching the injection solvent with your starting gradient conditions.

I am facing the same issue, did you find a solution?