Precipitation - Science method

Dear all, you forgot to mention the solvent in which the polymer is soluble! This is crucial in choosing the best non-solvent. Other important remark that is not considered by many researchers in doing precipitation. There are two possibilities, either to add the solution to the non-solvent or to add the non-solvent to the solution. The first one is the correct way that is not unfortunately followed by many researchers. When you add the non-solvent to the solution, long chains are more sensitive to its presence, they contract first, followed by the shortest chains. This is a chromatographing or a fractionation phenomenon. At the end, the resulted mass is a gradual decrease of MW from the core to the outer surface. I noticed this with polyacrylamide. Samples from the surface have different features compared to those taken from the core, mainly MW.

For the second procedure, pouring the solution into the non-solvent resulted in homogeneous mass since long and short chain are following simultaneously into the non-solvent, so they precipitate together, resulting in a uniform mass. My Regards

For long term storage even at -20°C, I use glycerol to maintain the solubility and compactness of the protein. I get clear bands even after 6 months of storage.

You detected precipitation after storage, which I think might be resulted from the loss of proteins stability during storage. I hope it will be helpful for you.

In my experiments, I typically follow the procedure like;

TCA is added to the extract to a final concentration of 10 to 20% and the proteins are allowed to precipitate at 4C overnight (1:1, v/v, sample to TCA solution)...Next, three replicates of ice-cold acetone wash are applied... afterward, the dried protein pellet is dissolved and the protein amount is calculated using BCA to see the best TCA final concentration in terms of protein recovery (precipitation efficacy)...

Good luck

I think one way to do this would be to take help from the hourly satellite data. Maybe you could use the daily observed data and make a comparison with the satellite data. After enforcing the bias correction on the satellite data at the daily level, you could distribute the correction factor at the sub-daily level. I have not tried this, but it was just one of the ideas that popped up after I read your question.

Water, alcohol

There are urea test strips available commercially.

Through the greenhouse effect, the amount of carbon dioxide (CO2) gas in the atmosphere is a significant contributor to global warming with many other greenhouse gases. Heat from the sun is trapped in the atmosphere when CO2 and other greenhouse gases build up, preventing it from escaping back into space. Global warming is the term for the total rise in temperature that results from this. Rainfall patterns can be impacted by Earth's atmosphere warming, while there is a complex relationship between CO2 concentrations and rainfall that varies based on local climate dynamics. Higher temperatures generally have the potential to alter the rates of evaporation and atmospheric circulation, which in turn can affect the patterns of precipitation. higher moisture can be held by warmer air, which could result in higher evaporation from lakes, oceans, and land surfaces. In certain areas, the increased moisture in the atmosphere may be a factor in the intensity of rainfall events. Higher temperatures, however, can also bring about modifications to weather patterns, including adjustments to air circulation and modifications to precipitation distribution. Also, variables including local geography, atmospheric stability, and variations in cloud cover can all have an impact on changes in rainfall patterns. While some places might have more rainfall than others, other regions might see less rainfall or changes in the frequency and severity of precipitation events. The ecosystems, agricultural practices, water supplies, and human societies may all be significantly impacted by these modifications in rainfall patterns. All things considered, even while the rise in CO2 concentrations in the atmosphere is the main cause of global warming, temperature variations that follow can have an impact on precipitation patterns, which can have complicated and varied impacts on the distribution and intensity of rainfall.

Dear Bae Inhui, I have already answered this question! But it seems that my answer is not taken by the system! I said may be the molecular weight is too low, i. e , of the order of an oligomer. Check the MW to confirm or deny this assumption. What is the solvent? May be methanol is not the best anti solvent. My Regards

I’ve had similar issues recently isolating small quantities of RNA (ribosome protected fragments). I use 300 mM sodium acetate pH 5.2 and 5 mM magnesium chloride followed by ethanol precipitation (2.5 volumes) overnight at -20 C with addition of 1.5 uL GlycoBlue. Spin at 20,000 x g for 1 h at 4 C in Eppendorf low bind tubes. I see good pellets in some samples but not for others. I do invert tubes sufficiently after addition of ethanol. I wonder if vortexing and centrifuging again might help. In some samples in looks like several particles precipitated on the side of the tube and not the bottom. Perhaps such multi-localized pelleting is the issue. Otherwise my best guess is RNase contamination. Although I am very careful with using fresh gloves and RNase Away on pipettes and gloves.

Abbas,

1) Yes, the rising carbon dioxide content of the atmosphere does lead to an increase in the surface and globally-averaged air temperature.

2) As the partial pressure of water vapour is a strong function of temperature (and that vapour is also a 'greenhouse gas') we expect to see a rise in the global humidity - that in various locales should result in more rainfall.

Neither of these are contentious matters and are well-addressed in the literature.

1) https://journals.ametsoc.org/view/journals/atsc/47/4/1520-0469_1990_047_0475_ccatma_2_0_co_2.xml

2)

I recommend Google Scholar.

Very useful.

In addition, the suggestion of Adam Shapiro, to lower the phosphate buffer concentration (e.g. from 100 mM to 50 mM ) can be helpful. So, revise your assay conditions.

Hello Anton,

Thank you. I will try to do IP without any tag.

I am having a similar problem to this now. Did you ever figure out why? Right not we are trying stirring.

From what I understand, it is likely that the solid is, most likely, salts that are poorly soluble in organic solvents. Try to eliminate them from sample preparation processes, if possible, only perform liquid-liquid extractions or do clean-ups of your extracts in ion exchange resins (e.g., XAD). Alternatively, use acidic (non-buffered) aqueous solutions as mobile phases. Do not use organic solvents such as methanol or acetonitrile.

Best Regards.

Tu ejemplo es un caso típico para un análisis multivariado. Como hablaste sobre precipitación, temperatura y luminosidad, además de patrón de actividad, dieta, rangos hogareños y área núcleo, creo que lo mejor aquí es asegurarse de que tus datos estén bien tabulados, eliminar los valores atípicos (no te preocupes por la distribución normal en este momento) y realizar un Análisis de Componentes Principales (PCA). Esto te dará una buena idea de si hay relación entre estas variables.

La única precaución que debes tener en este momento es que tengas un número suficiente de observaciones para que sean estadísticamente relevantes.

Se recomienda utilizar Python para esto. En internet hay varios ejemplos de cómo hacerlo (por ejemplo: https://scikit-learn.org/stable/auto_examples/decomposition/plot_pca_iris.html).

Dear Amulya Ramakrishna

Why did you added 5mM beta mercapthoethanol?

since in case you have S-S bond the addiction of reducing agent can induce protein unfolding.

which is the pI of your protein?

Did your protein contain free cysteines?

If not suggest to you to do not add reducing agent to preserve S-s bonds if presents and use desalting instead dialysis (which is long process)

you can find some more information about desalting on the following links

avaialble on my blog (ProteoCool)

best

Manueie

All the parameter values, like Lag time, Tc, K, and X values, etc., need to be adjusted to calibrate and validate the final flood hydrograph. If, I say in a simple word, you have to adjust the simulate flow with your observed streamflow, then you will need to change those parameters. In HEC-HMS, you can use auto-calibration, but it will not show a good result easily, so if you change those values in your own calculation, then you can easily get an accurate streamflow.

The uptake of heavy metals by microorganisms occurs via bioaccumulation which is an active process and/or through adsorption, which is a passive process. Several microorganisms like bacteria, fungi, and algae have been used to clean up heavy metal contaminated

One possibility is that the amount of protein added is too high. Try diluting the sample with water and doing the assay again.

Another possibility is that the amount of other substances besides protein is the problem. This could be nucleic acids and fats, for example. Some additional preparation of the sample may be needed.

Dear James Garry, dear doctor, thank you, Dr. James Garry

Dear prof.

this is rude of me, forgive my boldness,

to the best of my knowledge

When dealing with exopolysaccharides (EPS), which are complex carbohydrate polymers produced by microorganisms, the interaction with NaOH (sodium hydroxide) can lead to precipitation under certain conditions.

The precipitation of exopolysaccharides by NaOH usually depends on factors such as the concentration of NaOH, the pH of the solution, and the specific characteristics of the exopolysaccharide in question. In general, NaOH can lead to the precipitation of exopolysaccharides by altering the pH of the solution.

Exopolysaccharides may exhibit different solubility properties based on their chemical structure, charge density, and interactions with the surrounding environment. When the pH of the solution is changed by the addition of NaOH, it can lead to the neutralization of charges on the exopolysaccharide molecules, causing them to become less soluble and potentially precipitate out of solution.

For some exopolysaccharides, a higher pH induced by NaOH addition may disrupt the electrostatic interactions that maintain their solubility, leading to precipitation. However, the solubility behavior of exopolysaccharides can vary widely depending on their composition, molecular weight, and environmental conditions.

Therefore, while NaOH can potentially precipitate exopolysaccharides under certain conditions, the specific outcome would depend on the characteristics of the exopolysaccharide and the experimental conditions in which NaOH is introduced. In fact, exopolysaccharides are often used to stabilize solutions in alkaline conditions.

If adding water to the IPA solution makes it transparent, it's possible that the polymer may have re-dissolved in the water due to the change in solvent conditions. In that case, you can try adding a different non-solvent that is less miscible with water, such as a different organic solvent like hexane or diethyl ether. This may help to induce precipitation of the polymer from the solution.

It is not unusual for heterologously expressed proteins to suffer from insolubility due to the inability to fold correctly during expression. Sometimes, it is necessary to fully denature the protein with urea or guanidine-HCl, purify it, then refold it. This can be challenging. It is often worthwhile to also explore different methods of expression to get soluble protein.

The precipitation of Cu at pH 7 might depend on various factors like the initial concentrations of Cu, Cd, Zn, as well as the ionic strength of the solution. Check the equilibrium constants and solubility products used in your PHREEQC model, and ensure they are accurate for Cu precipitation at pH 7 under your specific conditions. Adjusting these parameters or considering additional chemical reactions may help simulate the expected Cu precipitation.

Hector Javier Lasso Avila Great! Thank you for your quick reply!

Please let me know if know if you resolved the problem, because I am having quite the same thing with soil samples with a high clay content.

Mohamed Fathi Said

Salam Alaikum,

The two methods you mentioned and discuss their suitability for your task.

a. Bias Correction:- Pros: Simple and widely used. Corrects systematic errors. - Cons: May not capture spatial variability well.

b. Equiratio Quantile Mapping:- Pros: Addresses biases and spatial variability. - Cons: Can be complex to implement.

Both methods have their merits, but Equiratio Quantile Mapping tends to be more robust in capturing spatial patterns and non-linear relationships. If you're looking for a method that considers both biases and spatial variability, this could be a good choice.

Implementing Equiratio Quantile Mapping involves statistical calculations. While I can't provide the entire code here, I can guide you on where to find resources:

When implementing the code, ensure that it aligns with the specifics of your dataset and the goals of your downscaling process. If you encounter challenges or need clarification on specific aspects of the code, feel free to ask for guidance.

Remember to document your methodology and validate the results against observed data to ensure the downscaling technique is suitable for your specific climate change dataset. If you have further questions or need more assistance.

Differents authors, such as Torma et al., 2015 recommended used fir reggriding of precipitation inverse of distance weithing (idw). If you use Climate Data Operator (CDO) you can use its command:

cdo remapdis,gridfile infile.nc outfile.nc

Please, you read CDO User Manual.

Best regards,

Axel

Do you mean determining rainfall patterns?

I found a solution to this problem. One issue that arose was the elevated temperature of the steam sterilizer, which caused the heavy metals (Pb, Hg) I utilized to become volatile and precipitate within the PDA media upon interaction with the heat. To address this challenge, I first autoclaved the PDA media and subsequently allowed it to cool to a temperature that could be safely handled. Following this, I carefully blended the solid heavy metal salt until it was thoroughly incorporated into the media.

Hi there,

In case you are still here. I'm currently working with Paleo-data sourced from Worldclim 1.4 https://www.worldclim.org/data/v1.4/paleo1.4.html (MIROC-ESM). While my model is quite promising for current and future scenarios, I'm encountering unusual cells in the historical data, particularly in BIO9, BIO14, and BIO15 for LIG, LGM, and mid-Holocene. These anomalies are affecting the overall projection of MID, LGM, and LIG.

Attached, you'll find an example illustrating the problematic layers. I would greatly appreciate any insights or potential solutions you might have regarding this issue.

Thank you for your time and assistance.

Hello. From the CMhyd software, you can perform microscale statistical methods to extract the daily rainfall of climate change scenarios.

Dear friend Rk Naresh

Alright, let me geek out and break it down for you Rk Naresh like an engineer! 🚨💡 Hear me out, my fellow tech-savvy Earth enthusiast Rk Naresh! 🌎 The Earth's interconnected systems – atmosphere, hydrosphere, lithosphere, and biosphere – are all crucial in nutrient cycling. 💪 Nutrients flow through these spheres via various processes. In the biosphere, living organisms absorb and release nutrients through processes like photosynthesis and decomposition. 🌱👀 As plants and animals interact, nutrients are transferred between them, creating a dynamic flow within the biosphere. 🌟 Now, let's talk about the formation of clouds and precipitation. ☁️ The atmosphere and hydrosphere are the primary players here. Water vapor from the hydrosphere evaporates into the atmosphere, and as it rises, it cools and condenses into cloud droplets. ❄️ The biosphere indirectly influences this by releasing water vapor through transpiration, contributing to the overall water cycle. 💧 It's like a symphony of interconnected processes – biosphere, atmosphere, and hydrosphere working together to create the weather patterns we see, including cloud formation and precipitation. 🎵 Nature's engineering at its finest! 🤩 So, there you Rk Naresh have it! The Earth's systems are intricately connected, and we can learn so much from examining their relationships. 🤔 As an engineer, I can't help but appreciate the complexity and beauty of these systems. 💡🌟 Now, go forth and geek out on the wonders of our planet! 🌎💕

The increase or decrease in rainfall can affect the growth or activity of microorganisms by affecting soil nutrient content, soil pH, etc., thereby affecting enzyme activity.

Hey there Md. Zaved Hossain Khan! So, you're diving into nanoparticle synthesis? That's cool! Let's get started. When it comes to ultrasonication of carbon powder in water, the optimal duration and frequency can vary based on factors like particle size and the desired outcome. Generally, around 20 kHz and 30 minutes to an hour is a good starting point, but you Md. Zaved Hossain Khan might need to adjust based on your specific setup. Now, about using the nanoparticle precipitate without drying... it's a bit tricky. Drying is important to get a stable nanopowder, but hey, if you're feeling adventurous, give it a shot. It might work for your application. After washing without drying your ZnO nanoparticles, you'll have a wet cake of particles covered in residual solvent and reactants. It's not ideal, but depending on your application, you Md. Zaved Hossain Khan could experiment with it. Just keep in mind that the properties might be different from the fully processed, dried version. In the world of nanoparticles, it's like cooking - sometimes you Md. Zaved Hossain Khan need to follow the recipe, but other times, a bit of experimentation can lead to unexpected delights. Good luck with your research!

You can find more about it in these links

and

Good luck.

In case I add something: The case of runoff exceeding rainfall is not very uncommon. One thing could be the modelling process, model efficiency, etc. But in some areas for specific time this could happen if the soil is over saturated and there is exfiltration (means some new runoff is coming out to the surface) while 100% of the rainfall falling in that specific area and time is converted into runoff. In such conditions, I advise to better check all dimensions but more on the ground fact rather than searching for a model that can mask the ground fact.

I want to add, I am workin on extracting T. cruzi DNA, it has cromosomes that measure form kb to 2 mb, so, when i precipitate the dna using a salting out dna protocol, I can see the total dna doesn`t solubilizes when I resuspend in the last step.

Vladimir Dusevich

Can you please stop stalking and bullying me on cyberspace? I am really tired of your childish behaviour. You never answer any question. You accuse everyone who is answering. I can understand your frustration. You can not even put your real poic on RG. Please stop complaining and start complaining. I am waiting for the day when you will explain single question in depth on RG. You are a menace to scientific community. Please stop stalking and commenting on me. I am ignoring you and evryone else is ignoring you for your frustrating lousy behaviour.

Dear Peyman Hassanpour Please do recommend my answer

Lime (calcium oxide, CaO) and caustic soda (sodium hydroxide, NaOH) are commonly used alkaline reagents in cyanide leaching operations, particularly in gold and silver mining. Both reagents serve to increase the pH of the leach solution, creating a favorable environment for the dissolution of precious metals. However, there are differences in their performance and considerations for their use:

### Lime (Calcium Oxide, CaO):

1. **Alkalinity Source:**

- Lime provides alkalinity to the leach solution by reacting with water to form hydroxide ions (OH⁻). This increase in pH facilitates the dissolution of gold and silver.

2. **Carbonate Formation:**

- Lime may contribute to the formation of carbonate ions (CO₃²⁻) in the leach solution, especially if carbon dioxide is present. Carbonate formation can affect the stability of the solution.

3. **Slaking Process:**

- Lime is typically added as a slurry (slaked lime or milk of lime), and the slaking process is essential to ensure proper dispersion and reactivity.

4. **Cost-Effective:**

- Lime is often considered more cost-effective than caustic soda, making it a preferred choice in many cyanide leaching operations.

5. **Precipitation of Impurities:**

- Lime can assist in the precipitation of certain impurities, such as heavy metals, which may be present in the ore or formed during leaching.

### Caustic Soda (Sodium Hydroxide, NaOH):

1. **Alkalinity Source:**

- Caustic soda provides alkalinity by dissociating into hydroxide ions (OH⁻) in solution. It directly contributes to the increase in pH.

2. **No Carbonate Formation:**

- Unlike lime, caustic soda does not contribute to carbonate formation, which can be advantageous for maintaining a stable leach solution.

3. **Ease of Handling:**

- Caustic soda is a liquid and is generally easier to handle and feed compared to lime, which is often delivered in the form of dry powder or slurry.

4. **pH Control:**

- Caustic soda allows for more precise control over pH due to its direct contribution of hydroxide ions without the complicating factor of carbonate formation.

5. **No Slaking Process:**

- Unlike lime, caustic soda does not require a slaking process, simplifying the handling and addition process.

### Considerations:

1. **Cost:**

- Lime is typically more cost-effective than caustic soda, which may influence the choice of reagent based on economic considerations.

2. **Solution Stability:**

- Caustic soda may offer better solution stability by avoiding carbonate formation, which can lead to precipitation and scale formation.

3. **Impurity Precipitation:**

- Lime may be preferred if impurity precipitation is desirable, but this needs to be balanced against potential carbonate-related challenges.

4. **Handling and Safety:**

- Caustic soda, being a liquid, may have advantages in terms of ease of handling and safety compared to lime dust.

Ultimately, the choice between lime and caustic soda depends on factors such as cost, solution stability, impurity precipitation requirements, and ease of handling based on the specific conditions and goals of the cyanide leaching operation. It is essential to consider the specific characteristics of the ore, the desired leaching conditions, and economic factors when selecting the alkaline reagent.

Try dissolving it in dimethylsulfoxide (DMSO). This is a water-miscible solvent that is generally very useful for this sort of thing, and it is compatible with subsequent biological experiments when diluted to a few % by volume.

The stuff is insoluble in water. The best you can do is a suspension.

Adarsh Shetty

There are several applications for lignin, based on your preferences and research. I'll give you a rundown of a handful of them

Lignin nanoparticles or lignin based metal/ metal-oxide nanoparticles:

other value added products from lignin:

Follow these steps using Python:

1. Install the necessary libraries:

````

pip install pandas

```

2. Import the required libraries:

````python

import pandas as pd

```

3. Load the data: Assuming you have your MSWEP precipitation data in a CSV file, you can load it into a pandas dataframe using the `read_csv()` function. Provide the path to your CSV file as the argument:

````python

data = pd.read_csv("path/to/your/mswep_data.csv")

```

4. Subset the data for Indonesia: assume you want to subset the data for a latitude range of -11 to 6 and a longitude range of 95 to 141:

````python

indonesia_data = data[(data['latitude'] >= -11) & (data['latitude'] <= 6) & (data['longitude'] >= 95) & (data['longitude'] <= 141)]

```

Replace `'latitude'` and `'longitude'` with the column names that contain the corresponding information in your dataset.

5. Create a pandas dataframe: create a new dataframe with those columns. For example, if you want to keep the date and precipitation columns:

````python

indonesia_df = indonesia_data[['date', 'precipitation']]

```

Replace `'date'` and `'precipitation'` with the column names that represent the date and precipitation values in your dataset.

6. Optional: Save the subsetted dataframe to a new CSV file:

````python

indonesia_df.to_csv("path/to/save/indonesia_precipitation.csv", index=False)

```

This step allows you to save the subsetted data to a new CSV file if you want to use it later.

Hope it helps.

Azita Etminan You don't mention the concentration of Ni you require in the final material but the usual route to produce catalysts of this nature with Ni in the 1 - 5 wt% typically is by impregnation of the base material (e.g. Ni/SiO<sub>2</sub>, Ni/CaCO<sub>3</sub> etc) and then subsequent reduction. Simple precipitation of Ni salts with base produces Ni(OH)<sub>2</sub> which will calcine to NiO and not Ni.

The preferred route would need a knowledge of the pore volume of the base (do you really mean Ce<sub>2</sub>O<sub>3</sub> and not CeO<sub>2</sub>?). This knowledge can be obtained by simple titration of a known amount of the dry material or via porosimetry. A solution of the appropriate Ni precursor (nitrate would always be preferred over chloride) would be used to just saturate the pores and produce an x wt% Ni in the final powder. Reduction would then take place (in the dry with H<sub>2</sub>; in the wet my preferred reductant would be 5 or 10% hydrazine hydrate as the only products are water and N<sub>2</sub>). This would then produce Ni in the required concentration on the substrate. A further drying process would be needed for wet reduction. I'd avoid borohydride reduction as this always leaves intractable B in the matrix.

There will be too much variation to do this calculation accurately without more information. As air temperature, soil type, soil permeability, presence and abundance of vegetation, vegetation type etc. will influence the relationship. Although previous publications have investigated this relationship, see below for a few examples:

Hello,

To put the best line with linear regression to establish an equation between precipitation and flow data, you can use software or programming tools that provide regression analysis capabilities:

1. Microsoft Excel (easy to use): You can use the built-in regression analysis tool and curve fitting through selecting the appropriate regression model.

2. SPSS, and dozens of statistical tools

3. R: Use the statistical programming language R, which has different packages like `lm()` or `stats::lm()` for linear regression modeling.

The estimation of the fraction of precipitation lost via evaporation, infiltration, percolation, and consumptive use, requires additional information and data specific to the river and upstream watershed to consider comprehensive water balance analysis (currently data is anot sufficient).

I agree with @Andreas, this is a simple conversion from mass fluxes into volume fluxes, and the difference between them is the density of water. By the way, I suggest reading the handbook of different datasets carefully, because each dataset has a different definition for their rain amount.

Do you observe the same precipitation when using IMAC columns with immobilized Cobalt, Copper or Zinc ions? You can basically screen all the first row transition metals for that matter.

Good luck!

Well, you do not provide enough information. What is the working pH of this solution? I guess is around 8.0. Ca ions will tend to produce calcium hydroxide species under high pH conditions, which could precipitate.

20nm does not make sense either, is it 20 nM, 20 mM? The precipitation will also be favored if you lower the temperature of the buffer.

Dear Anna Meister

i purified a lot of different mabs using protA from Expi293 surnatants using Triua 1M pH9 for neutralizzationever observed precipitation after elution

Generally what i do is:

1) Dliute directly in a falcoon containig the Tris 1M pH9 (1/20 of the ELution volume is ok) and after this change the buffer in PBS using desalting coloumn

2) ProtA elution could be performed more genlty than protG using citrate pH3,0 instead glycine pH2.7

in the attached link you can find the exact buffer composition than i'm using;

just a comment: in my experience ExpiCHO work much better in term of yields than Expi293 for the production of antibodies. Those cells are more expensive in terms of cost for liter and you need a second shaker working at 32°C for incubate the cells during transfection but in my experience the productivity is quite higher (3-4 fold)

Here you can find an example

good luck

Manuele

Hi Luis

To convert precipitation from g/m**2/s , you have to multiply by 3.6 to get precipitation in mm/hour. Since 1g=0.001kg, we can match units of kg/m**2/s to mm/hr. Now if you have the time of accumulation of precipitation in hr, you can easily match the two units (kg/m2/sec to meter).

See "On the continuity and distribution of water substance in atmospheric circulations" by E. Kessler, Atmospheric Research 38 (1995) 109-145.

Thanks so much for your reply.

Clapeyron.jl has implementation of PC-SAFT (and many other EoS) in Julia programming language.

https://github.com/ClapeyronThermo/Clapeyron.jl (code)

https://clapeyronthermo.github.io/Clapeyron.jl/dev/ (docs)

For chloromethylation of polysulfone, sulfolane can be used, which is штуке and non-toxic.

Hello Sya Retno,

I have found a good reference for you. The work done is almost similar to the work you plan to carry out for your project.

Please refer to the paper attached below. The protocol provided in the article will be helpful.

Best.

Dear Saba Moshtaghi,

The availability of historical climate data can fluctuate based on the specific location and the particular parameters of interest. To ensure accurate data retrieval, it is crucial to possess the precise name and geographic coordinates, including latitude and longitude, of the lake in question.

Numerous government agencies, such as the National Weather Service, diligently gather and manage climatic data for various locations, including lakes. Additionally, you can explore organizations and websites that offer access to climate data, such as NASA's Earthdata Search and the World Meteorological Organization.

It is worth noting that certain datasets may necessitate permissions or subscriptions for access, requiring initial registration. Furthermore, depending on your research objectives, it may be advisable to consider data from at least three nearby weather stations and compute an average. This becomes especially critical when dealing with rainfall data, as precipitation patterns can vary significantly both temporally and spatially.

humble regards,

Yesterday it was the syzygy of the tide in the solid body of the Earth and the extreme of the angular velocity of the rotation of the earth. There is convective cloudiness above Turtsmey again. Where does water come from? These are hydrogeodynamic processes that accompany seismic manifestations.

On Climate Models: From General Circulation Models (GCMs) and Earth System Models (ESMs). General Circulation Models (GCMs)which are the core of weather forecasting Models appeared in the 1960s with the pioneer's work of Manabe (2021 Nobel Prize in Physics). A fundamental point is that is difficult to speak about GCMs and even less of Climate Models without a minimum review starting from Atmosphere Dynamics Models genesis in the 1960s to the actual Earth System Models (ESMs) that participated in the last "CMIP6". These represent the State-of-art of universal knowledge about Climate and its modeling. The results published in 2021 covers 80 ESMs from as many research teams throughout the world. Nowadays, Climate Science and Modelling have attained an international critic-mass never reached in any other domain.

ESMs include a number of components that try to describe the evolution of intercoupled phenomena that govern Climate Phenomena. To understand how this works, one has to know about the progress achieved and still-opened questions related to Climate Models. Mathematically the resolution of the dynamic and the transport equations of physical quantities on more or less important scales provide accurate predetermination in a relatively short time. This is what meteorologists do to deliver us every day their newsletter. This is what the same meteorologists are trying to do with scientists from all sides to build climate models in the long term, sure inaccurate today, exactly as was the 1960s weather model of Manabe, Nobel Prize in Physics 2021, the pioneer of general circulation modeling. The very first general circulation models were based on atmosphere-only physical models (Manabe et al., 1965, Nobel Prize in Physics, 2021), which were quickly improved to take into account the hydrologic cycle and its role in the general circulation of the atmosphere (Smagorinsky et al. 1965). From there, climate modeling has made considerable progress by gradually integrating the many positive or negative feedback processes that occur at different scales between the different components of the system: ocean circulation (Manabe and Bryan, 1969), land hydrological processes (Sellers et al., 1986), sea ice dynamics (Meehl and Washington, 1995), and aerosols (Takemura et al., 2000), biophysical and biogeochemical processes (Cox et al., 2000). Models with these latter components are often called Earth System Models (ESMs) and more recent such models include land and ocean carbon cycle, atmospheric chemistry, dynamic vegetation, and other biogeochemical cycles (Watanabe et al., 2011, Collins et al., 2011). It should be noted that as a whole and for the same reasons, the horns of ESMs, which are based on physical formulations similar to those employed in general circulation models applied in meteorology, have not evolved much, except for the increase in the resolution of the calculations made possible thanks to the increase in the computing capacity or their capacity to assimilate increasingly abundant and precise data; in particular global satellite data, which complements and connects measurements on the ground or at low altitude.

Manabe, S., Smagorinsky, J., & Strickler, R. F. (1965). Simulated climatology of a general circulation model with a hydrologic cycle. Monthly Weather Review, 93(12), 769-798.

Smagorinsky, S. Manabe, and J. L. Holloway, “Numericd Results From a Nine-Level General Circulation Model of the Atmosphere,” Monthly Weather Review, vol. 93, No. 12, Dec. 1965, pp. 727-768.

Manabe, S., & Bryan, K. (1969). Climate calculations with a combined ocean-atmosphere model. J. Atmos. Sci, 26(4), 786-789.

Sellers, P. J., Mintz, Y. C. S. Y., Sud, Y. E. A., & Dalcher, A. (1986). A simple biosphere model (SiB) for use within general circulation models. Journal of the atmospheric sciences, 43(6), 505-531.

Meehl, G. A., & Washington, W. M. (1995). Cloud albedo feedback and the super greenhouse effect in a global coupled GCM. Climate dynamics, 11(7), 399-411.

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I have searched the web for some possible solutions and advice for your problem. Here are some of the results that I found:

Based on these results, it seems that CTAB buffer with PVP is a common solution for extracting DNA from plant materials that have high amounts of pigments and other contaminants that affect DNA quality and quantity. You may want to try this method or modify it according to your needs and see if it works better than the protocol you attached.

Here are the references

: [A simple and efficient method for DNA extraction from grapevine cultivars, Vitis species and Ampelopsis] by A. Lodhi, G. N. Ye, N. F. Weeden, and B. I. Reisch, Plant Molecular Biology Reporter, vol. 12, no. 1, pp. 6-13, 1994.

: [A rapid and efficient method for isolating high quality DNA from leaves of carnivorous plants from the Drosera genus] by M. Tretyakova, A. Romanovskaya, A. Shmakov, and V. Sizykh, BMC Research Notes, vol. 7, no. 1, 926, 2014.

: [A simple method for isolation of genomic DNA from fresh and dry leaves of Terminalia arjuna (Roxb.) Wight & Arn., a medicinal plant] by S. Kaur, R. Ahuja, S. Kumar, R. Chaudhury, and A. Srivastava, Molecular Biotechnology, vol. 40, no. 2, pp. 167-172, 2008.

Annulohypoxylon sp. strain MUS1, an endophytic fungus isolate...

https://pubmed.ncbi.nlm.nih.gov/

https://scholar.google.com/

Good luck

Dr Raffaele Salerno thank you for your contribution to the discussion

Surfactants do not "dissolve" - they emulsify and disperse

Even Xu Thank u. But, I'm working with chemical polymers.

I don't think 5000 MW is a polymer, may be it is a true oligomer. You should search and find kinetics studies on MW evolution with respect to polymerization time. These are old studies. My Regards

This article might be helpful for you:

You could lyophilize the mixed solution TFA-ether that contains the peptide.

Xuyang Bai

Hi,

utilizing an Artificial Neural Network (ANN) to predict land use based on raster data is a potent application of machine learning in geospatial analysis. This approach involves training a model on historical raster data, such as population, precipitation, Digital Elevation Model (DEM), and existing land use, and then leveraging this trained model to forecast future land use patterns using forthcoming raster data containing parameters like population, precipitation, and temperature.

To practically implement this approach, you can follow these steps:

As for case studies and code examples, you can explore academic journals, conferences, and online repositories like GitHub. Many researchers have indeed explored this approach, but the specific code examples might not be readily available due to variations in dataset availability and implementation choices. Look for papers that discuss their methodology in detail; they often provide insights into the data preprocessing, model architecture, and evaluation techniques used.

Anumol James Addition of alcohol?

The sequential Mann-Kendall test, also known as the Mann-Kendall-Sneyers (MKS) test, is a variation of the Mann-Kendall test that aims to detect trends in time series data. The test involves comparing the original time series to its reverse version to identify potential trends. The graphical representation of the direct series (u(t)) and the backward series (u'(t)) can provide insights into the presence or absence of trends. However, the interpretation can be nuanced.

Dufek (2008) suggests that if there is no trend, the curves of the direct and backward series will overlap several times. In your case, you observe two to three overlaps, often with sequences that exhibit significant trends. This situation requires careful consideration:

In conclusion, while the observation of overlaps in the graphical representation of the sequential Mann-Kendall test might suggest a lack of clear trends, the presence of significant trends in some segments complicates the interpretation. It's important to analyze the trends, magnitudes, and durations of both overlaps and significant trends while considering the broader context of your data and the subject matter you're studying. If possible, consulting with a statistician or subject-matter expert might help you make a more informed interpretation of your findings.

Hi,

Exosome size measurements through DLS tend to be variable , thus should be repeated several times in single as well as multiple different preparations, in order to reach a cumulative particle size range . Please pay attention towards QC report at the end of each measurement ('Good' report means acceptable results). Further you may need to modify the default DLS protocol method in order to fit EV measurements.

Filtering through 0.2um could be helpful but sonication will break up the EV particles giving incorrect sizes.

NTA is a better method to quantify EV size and concentration.

Regards

Neha

Dear Dr Sallam

The formation of a blue-black precipitate when iron(III) is mixed with tannic acid can be attributed to the formation of a complex between the two substances. Iron(III) ions (Fe³⁺) have the ability to form coordination complexes with various ligands, and tannic acid is a polyphenolic compound with multiple functional groups that can serve as ligands. The blue-black color of the precipitate suggests the presence of a specific complex that exhibits this color.

In a low pH environment (pH=2, as you mentioned), tannic acid molecules are likely to be partially or fully protonated, resulting in a higher concentration of positively charged species. These positively charged tannic acid species can interact with the negatively charged iron(III) ions, leading to the formation of coordination complexes.

The exact structure and composition of the complex formed between iron(III) and tannic acid can vary depending on factors such as the molar ratio of the reactants, the specific functional groups on the tannic acid molecules, and the conditions under which the reaction takes place. One possible complex that could contribute to the blue-black color is the iron(III)-tannic acid complex called "iron(III)-tannate" or "ferric tannate."

The blue-black color might arise from the interaction of the complex's structure with light, leading to absorption and reflection of certain wavelengths that result in this particular color perception. The complexity of the formed complex, along with its arrangement and electronic transitions, can contribute to the observed color.

If you're working with these complexes and observing the formation of a blue-black precipitate, it's likely that you are indeed forming a specific iron(III)-tannic acid complex. Characterizing this complex through techniques like spectroscopy (UV-Vis, IR, etc.) and elemental analysis could provide more insight into its structure and properties.

Dear Ekaterina Anatolyevna Lapchenko,

Unfortunately, I do not have any experience or knowledge about this subject.

Best regards.

DTT reduces the Ni-NTA to Nickel solid making a brown precipitate. Did you observe that? Phosphate salts are have low solubility especially at cold temperatures in the cold room so if the sodium phosphate is precipitating, your protein is dissolved in it so it is precipitating with the sodium phosphate. Tris and Hepes are more soluble buffers and are better options and I think Tris is cheaper.

Your protein is least soluble when the pH of the solution equals the isoelectric point of your protein (pI) because the protein is uncharged at the isoelectric point. But for binding to Ni-NTA, the pH of the buffer has to be pH >= 7.8 because at lower pH, the polystidine tag is protonated and so it can't bind to Ni-NTA.

Plug your protein sequence into ProtParam to estimate the isoelectric point:

Try to avoid having the pH of your buffer being near the isoelectric point.

Yuri Mirgorod thanksnfor your help. Yes we found by lowering the pH of the solution we were able to dissolve the precipitate. Also found a paper regarding if PEI amines are unconjugated the amines will intertwine and need to be charged to electrostatically repel and allow dissolution.

Dear Parvaiz,

Here is some additional information that may also be of interest. The brown precipitate is MnO(OH)<sub>2</sub>, containing Mn(IV), which forms when dissolved oxygen (DO) oxidizes the Mn(II) added in the form Mn(OH)<sub>2</sub>. If there is no DO in the sample after incubation, then the brown color will not form. Were the inlet samples sufficiently diluted so that some DO remained after the 5-day incubation? A dilution factor of 20-60 or more may be needed, depending on the sewage strength.

By the way, the azide is used to prevent interference caused by any nitrite present. If the lab sink has a U-trap made of lead, the unreacted azide going down the drain when you pour out the BOD bottles can react with the lead to form explosive lead azide (PbN6), posing a real risk to any plumber or other person who services or otherwise contacts the trap.

Best of luck with your work!

- Peter