Detection - Science topic

Prepare solutions of the antibiotic at various concentrations in the desired solvent. Use a spectrophotometer to measure the absorbance spectrum of the solutions over the desired range of wavelengths. Make sure to use a clean cuvette, which should be quartz if you plan to make measurements in the UV range. Make sure the samples fill the cuvette.

Discard the concentrations that go off-scale for the instrument. A maximum absorbance of about 1 is desirable. Subtract any background from the solvent.

Choose the wavelength of interest. This is usually the wavelength with the highest absorbance, but you may choose a longer wavelength if the maximal absorbance occurs very far down in the UV.

Plot a graph of absorbance versus antibiotic concentration at the chosen wavelength. Perform a linear regression through the data points. This is your standard curve. The slope of the standard curve is the extinction coefficient for the precise conditions used to make the measurements.

I am not sure but you will find your solution here https://herbnews.net/how-do-i-make-my-herb-garden-attractive/

If we see the series expansion of cos function then how can we find the linear interval? After first term every term is non-linear. I am attaching the series expansion of cos function. If I am missing something in this concept please let me know.

Have you considered the wfdb-python library (https://github.com/MIT-LCP/wfdb-python)? It has QRS evaluation support. It is a pure Python library. Hence it runs on all platforms without any need for compilation. In particular, in the wfdb.processing library, there is a function wfdb.processing.compare_annotations, which can be used to compare the reference annotations against a test annotation generated by your detector.

The demo notebook provided by them describes how to use it: https://nbviewer.org/github/MIT-LCP/wfdb-python/blob/main/demo.ipynb

In particular, you provide

- reference annotations [the list of indices at which R peaks have been marked]

- test annotations [the list of indices at which your QRS detector has detected R peaks]

- the window width where you allow a matching annotation to be found. Thus there may be a difference of a few samples/indices in the reference and your algorithm's annotations

- the ECG signal on which the annotations were done.

The function then identifies the true positives, false positives, and false negatives. It reports the sensitivity and positive predictivity of your detector. You can also access the list of indices for false positives and false negatives for further analysis.

Following is the rough sample code:

comparitor = wfdb.processing.compare_annotations(ref_sample=ref_sample,

test_sample=test_sample,

window_width=window_width,

signal=signal)

# print the results

comparitor.print_summary()

if comparitor.fp > 0:

print(f'False positives: {comparitor.unmatched_test_sample}')

if comparitor.fn > 0:

print(f'False negatives: {comparitor.unmatched_ref_sample}')

I think this article may be helpful

Hi,

Please check out following article.

Dear Murana Awad ,

etecting and Recognizing texts for a given natural scene image using EAST and Tesseract Algorithms.

Regards,

Shafagat

Considering the fluorescence origin, CDs were encapsulated in silica matrices by reacting a silane precursor with surface hydroxy groups that do not contribute to the emission process. It enables CDs in the uniform dispersion in silica to preserve their narrow emission in the solid state by avoiding aggregation.

To enhance the optical properties, stability and to preserve emissions

This is possible. I am working on it

i am interested too and follow

Dear Mohammed Y. Kamil ,

There are two main approaches in active contours based on the mathematic implementation: snakes and level sets. Snakes explicitly move predefined snake points based on an energy minimization scheme, while level set approaches movecontours implicitly as a particular level of a function.

Regards,

Shafagat

The Excalibur, which is built on top of camelot:

Python for Pdf: https://medium.com/@umerfarooq_26378/python-for-pdf-ef0fac2808b0

First check your protein reactivity in western blotting (both strep tag and his tag)

Confirm your protein is in soluble fraction. Usually proteins starts precipitating after dialysis and aggregates if your buffer composition is not suitable to protein.

If you explain little bit about your purification dialysis its helps us for better analysis.

Train dataset not available in this chalange..

" No training set is provided, participants should use their own data to develop a method. "

Test data can be downloaded from the  Zenodo platform

thank you all very much

Juan Adrian Ramos Guivar

Mohamed Farid Benlamnouar

Omkar Singh Kushwaha

Relation between milk with adulterants . Many times urea is added in the milk as adulterant to increase the density. If you is isolated then in FTIR it shows the absorption for NH2(V NH = 3200-3350 cm-1) and C=O) at (v 1700 cmi1).

Dipak Shinde Molecular Sieves %A column is only useful for elution of O2,N2,CH4 and CO when connected to TCD detector .Note That CO2 could not be detected using the same column. The detection limit of your detector needs to be validated first possibly by passing lowest concentration of products that you may form i.e CO gas.Secondly if your detector is sensitive enough to detect the concentration then injector volume,split ratio of injector can be changed to obtain desired peak for CO. Lastly the CO2 electro-reduction i.e. CO2 conversion could be very less which gives lower concentrations of CO which can be improved by process optimization. Find attached document which can help you to develop method for elution of CO and other permanent gases.

V. Carletti, A. Greco, A. Saggese, and M. Vento, "A smartphone-based system for detecting falls using anomaly detection," in ICIAP 2017 Proceedings, 2017. [Online] Available: https://link.springer.com/chapter/10.1007/978-3-319-68548-9_45

V. Carletti, A. Greco, V. Vigilante, and M. Vento, "A wearable embedded system for detecting accidents while running," in VISAPP 2018 - Proceedings of the International Conference on Computer Vision Theory and Applications, 2018. [Online] Available: https://www.scitepress.org/Papers/2018/66128/66128.pdf

Signal -noise ratio can be obtained from the Gaussian noise distribution

I am also looking same question description for my camera trapping data.multiple species and multiple season.

each camera duration of each species divide in a week.each week let's five presence of bears.you need consided 1.its means for first week your dectection is one..then check red fox week one..let's suppose five detention.but you need consided 1.then second week for each species and each season..RPRENCE or presence you can uses.Gamm. model you can used

I think it's circular reasoning because the detection probability is calculated using your criteria, which already include the detections!

Hi,

do you know the answer yet? how long could BrdU incorporated into DNA stay?

You can, however for less distance. And PixyCam is good for tracking colors. So if the moving object has any specific color, then things would become easy for you

Going to be challenging...how much material do you have? You could try pSer/pThr or pY combined with a protein specific IP...probably your best option.

Quite simple if your protein genuinely has a single phosphorylation, much harder to link to your phosphatase if there are more phospho sites (quite possible); if there are more, the contribution of your site to the whole protein phosphorylation might be lost in the noise. Ditto for the Phos-tag approach suggested above.

If you have enough material, mass spec might be your best option, although whether it will be quantitative enough to test phosphatase X contribution has to be empirically tested.

Good luck!

Matlab has an inbuilt function to remove haze: imreducehaze

Deeplearning approach can be great for future IDS. In general, IDS should be approached from a big data perspective.

Dear colleague,

Probably you mean the lateral flow immunochromatography slide tests, when you mention paper based techniques. The priciples are explained at the website https://www.abingdonhealth.com/contract-services/what-is-a-lateral-flow-immunoassay/

Concerning vitamin D3 or a D2 I know only only slide test that performs close to the usual testkit on the clinical laboratory analysers. I have tested it myselve and saw their EQAS results. I could bring you in contact with the supplier if you want me to.

This is a typical anomaly detection problem.

We've found great success in using Machine Learning tools such as Recurrent Neural Network (RNN) adopting the Long Short Term Memory(LSTM) algorithm. We've demonstrated its sensitivity and diversity on different kinds of heart diseases. We also found great application of the same methodology in other domain disciplines such as predicting and detecting anomaly in jet engines and oil rig machinery offline as well as real time.

When creating a malware, the creator has to think about malware-detection method first (otherwise his malware did nothing). => he created a malware that is not detectable with the current algorithms (anti-virus program). When a computer is infected, the defender analyzed the malware and creates a new mechanism to detect it. And again, the creator knows this mechanism and thinking of new malware. This cycle never ends, the battle between white hat and black hat hackers I would say

Hi Yesaswini, we have rich experience in the production of AAV with different serotypes. You could find a lot of information and protocol about on this website: www.genemedi.net/i/aav-packaging

You can also make extracts of your cells (all at the same cell "concentration") in an appropriate buffer (acidic pH) and measure cleavage of a caspase-1 specific substrate ( Amino Acid Sequence Ac-Trp-Val-Ala-Asp-pNA) in the presence and absence of a caspase-1 inhibitor. This would be the most sensitive method.

It is best to have multiple lines of assessment- WB, IF (FLICA) and cytokine release provide corroborative evidence and more likely than not, the reviewers would expect you to have more than one assay.

A description of your current protocol would help. How and when do you lyse your cells? What lysis buffer are you using and how are the lysates stored? What antibodies and buffers/reagents are you using?

We used to do LPS stimulations of innate immune cells and then look at various cell signalling pathways. In our experience, it is best to do a kinetic study concentrating on the first 6 hours following the addition of LPS. If you go in later, you may not see phosphorylation. We used to lyse our samples in Laemmli buffer. But RIPA buffer + protease inhibitors should work just the same. Once lysed, store the lysates at -20°C.

From what you describe, you have a problem with your blocking too. Try using BSA instead of milk for blocking as well as for diluting your antibodies. Some antibodies cross react with casein in the milk and can lead to the whole membrane glowing when you add the substrate. Also what antibodies are you using? We recommend using antibodies from Cell Signaling. Probe the membrane with the phospho-Ab first, strip it and then probe with the total Ab.

So to sum up, make sure you are analysing the signal early enough, you are lysing your cells correctly, are using the right antibodies and use BSA instead of milk.

I suspect that your question is too general. My wrist movement is somewhat independent of my body movement. For example, I may be playing a guitar and so my wrist will be moving in a certain manner. However, while playing the guitar I may be either, sitting down, standing up, walking or dancing to the music that I am playing. From this you can see that my wrist movements do not define my body movements and so one can not be inferred from the other.

Another situation where this "confusion" occurs can be manufactured if you have a chair that rotates. You can then sit in the chair with your wrist still while a friend quickly rotates the chair and (if you don't get dizzy) then if you look at the reading from your wrist sensor you will see that it seems to indicate that the wrist is moving (and it is but so is your whole body) and so you can not assume that a movement registered at the wrist only comes from the wrist.

This might be typically a problem of a wrong base line correction and a very low signal after amplification. As you can see the baseline fluorescence at cycle 1 is higher than yor plateau. This must differ at least one log scale; the plateau being 1 log higher. My first advice with respect to the baseline would be to set the cycles for baseline correction manually after e.g. cycle 6-8, or even later.

However, if you say that this is your normal control something must be really wrong. These curves really look like 'no amplification'. But first try to 'play' with the baseline correction. If this will not ameliorate your curves, you must indeed think of a damaged positive control (standard). The moreover you did change primers and probes. This can be simply tested I hope.

The other problem might be that your Taq polymerase is damaged; but I think thats unlikely.

So, one technical question. What is the signal and how does the amplification curve of your internal amplification (process) control looks like, or don't you use an internal control in you standards? I would strongly advice this in any case.

Hi Siddharth,

You got that right we are using a Background Subtraction Model , but a sophisticated one , combined with Wronskian Framework. This model was proposed by our Professor last year in " Gaussian Mixture Model Integrated with a Wronskian Framework for Local Change Detection in Underwater Video" research paper. Ya we are using pixel-level information to track our object of interest. This model can detect the stationary objects too, actually our model has two stages , first it takes some initial frames for background construction (we take such frames , where there is no movement), than comes the object detection stage , than we take frames of moving objects , to detect. Yup ,it will definitely fail if there is lighting variations . We will work on that in near future. I have already implemented (HOG+SVM) Framework + our Background Subtraction Algorithm , got false positive results in some frames . Thanks for a response.

- Sophomore student

We used the DNPH method at UCL, it works well and is quite simple if I remember. You could try adding potassium ferricyanide solution to a sample and running NMR as it chelates the Fe but not sure about the Ni. May be worth a try perhaps.

Discard

TB.......... if confirmed........ can reach and be found in any part of the body

Top view will generally be easier insofar as:

1. the viewing angle is perpendicular to the horizontal plane in which people are walking, giving good information about position in the horizontal plane;

2. there are no occlusions (underpasses, tree foliage, balconies that people can walk under, partially or totally blocking them from view at times;

3. there is consistent high contrast and low shadows from top-down lighting;

4. people keep a comfortable distance between themselves.

An angle view will be worse insofar as:

1. a low viewing angle generally loses information about distance along the line of sight;

2. there are occluding walls or signs, and insofar as one person occludes another in passing;

3. a crowd of many people of different size creates a continually changing, often low-contrast, background for a tracked individual.

If you had people who are strangers to each other (well separated), walking civilly on a uniformly illuminated white sidewalk with no overhead occlusions, then the tracking algorithm would be trivial. Any reasonable algorithm would perform reasonably well because contrast is high and confusables are few. This would not be true if you viewed the scene from a low angle.

Depending on the desired detection range, and available range and azimuth resolution, micro-Doppler signature might also help in differentiating UAVs from birds.

PbS QD is the most promising materials for optoelectronics application. 

The synthesis of this PbS QD is quite easy. You can read this paper (DOI: 10.1002/adma.200305395)

You can apply quantum inspired swarm intelligence. Since quantum based applications are multi-faced in nature, the algorithm can cover multi-modal aspect of intrusion. Quantum PSO can be one suitable approach. You can also substantiate the proposal with some mathematical convergence proofs. You can refer to an article on Evolutionary PSO.

UV and IR camera allows to measure intensity of photons flow at this wavelength.

You can,t chracterize food using thos 2 wavelength. You need spectro analysis and create solution of food

Dear Mr. Bhui,

You can still detect any kind of faulty input to the UKF system equations using the innovation sequence and an appropriate chi-square test applied to the innovation covariance of the filter.

For such detection your filter must be sensitive to the faults in the inputs (e.g. a filter with low process noise covariance, Q).

If there is a possibility for both measurement and input faults then you may need also a fault isolation process as you need to detect two types of faults both from the innovatiaon sequence. I suggest you to check the following publication in this case for the details of a fault isolation process

We applied the same isolation process for an estimation problem with the UKF in

If you have trouble accessing the full texts I can send them via e-mail.

Best regards

You might try Geogiga software, made for shallow exploration. One month free trial and not expensive to buy. Runs on Windows. 

Definitely take a look at the articles by Alexey Kamyshny, for using HPLC methods to characterize sulfur species. Sulfides can be easily measured using the methylene blue method (there are many colorimetric kits for doing this), though there are also good sensors out there for sulfide measurement as well. There are test kits for sulfite on the market, some of which are specifically intended for wine making. Measuring elemental sulfur can be a little more tricky. Voltammetry can be used to detect various sulfur species, including elemental sulfur. I've used x-ray absorption spectroscopy and Raman spectroscopy to detect elemental sulfur in my samples, though I wasn't intending quantification through those methods.

I experienced 2 approach for the solution :

- Radio manifacturer proprietary protocols used to implement a token passing based protocol, where the unit of time is divided by the # of active connected clients (TDMA), then each one has a time slot to transmit in. No starvation but latency introduced. A practical implementation (currently working on field ) is NV2 protocol that can be enabled on Mikrotik radio devices. 

- Indirect approach based on measuring the whole link usage assigned to the AP slowing down the usage of the nodes currently occupying the biggest % of the assigned badnwidth. Slowing down the node generating the biggest  flows will give to the other transmitting node the possibility to increase and improve transmission. The process is iterated to converge to the equalizing solution. This approach has the advantage to NOT require any proprietary protocol implementation neither any 802.1 modification becasue it's implementation indirectly regulate channel usage policy

Here a practical implementation of this concept:

hola Hector, tengo un paper de 1944 de jbc, pero medicion de arginasa, espero te sirva, saludos!

Multiple issues are related with heat treatment of milk. It depends on the type of protein and the range of heat treatment with the time of treatment.

In lab condition, it is also important to see the come-up time of steady-state temperature while doing the thermal treatments as it needs to be as quick as possible.

The following papers might be a good start to make a work plan-

Please consult Marketing Research books by any of following authors:

Andy Field

Naresh Malhotra

Hair et al. 

Upon charging/discharging CV tests of your Na half cell, then you increase the percentage of the nanosize Na-clusters in the Na negative electrode. Initially, in your Na half cell, the melting temperature of the Na bulk material is not dependent on its size (97.8°C :  bulk Na). However, as the dimensions of a material decrease towards the atomic scale, the melting temperature scales with the material dimensions. The decrease in melting temperature can be on the order of tens to hundreds of degrees for metals with nanometer dimensions. So, upon CV cycling tests of your Na half cell, then the nanosize Na-clusters increases to a high percentage; and a nanosize-Na melting might occur at a much lower temperature (<97.8°C); then a Na-nano-metallic short circuit (SC) can take place (synergy by gravity for Button Cells, also) at a high cycling number N_critical, while performing CV.

In short, a SC risk scales with the cycling number N.

Use Apoptotic kit and see nuclear staining. Sometimes late apoptosis is similar to necrosis,but early apoptosis is condensed nuclear chromatin sometimes  half-moon-shape. If you are able to perform semithin sections embedded in epoxy resin apoptotic pictures are fantastic!

Good work!

Dear Javad Ghorbani,

Look the link, maybe useful.

Regards, Shafagat

Thanks Rafael, I will try that!

Sir, could you please clarify the question littlebit more. Not getting propely i.e which context? Failure of what material mmc or d2?

Hello, 

Thank your for your response. 

For the detection of occlusion, i think that the tracking of the different object in motion is an accurate process and it doesn not rely on any a priori assumptions. Indeed, it is an automatic process. 

agreed with mushtaq ahmad

Hi Josep

Is very large to respond, however I'll link some references

How was your definition of detection obtained?

  - inspired by the ISO 7218, point 10.3.2.4.1,

How do you incorporate PCR efficiency,

  - one example is https://www.ncbi.nlm.nih.gov/pubmed/23649952

the DNA purification procedure and the efficiency of bacteria recovery in your determination of detection limit?

  - it must to be experimentally evaluated or using internal standards

How do you ensure your PCR setup is appropriate to apply the detection limit?

  - it mus be done during the method validation

How do I calculate the detection limit in qPCR?. Available from:

Hi Anna, if you want to go "old school" in determining Ca++ in your solutions, then you can use Ksp for Ca++ solubility with various anions:

Ok thank you very much Sir for the help

Is your candidate protein a soluble protein? You might have an issue seeing the protein on a western blot if the candidate protein is localized to the plasma membrane or the ER or maybe even the chloroplast. I would try adding 1 - 0.1% Triton x-100. It increases the overall amount of protein I'm able to see on my westerns. If it is localized to a membrane you might want to try ultra-centrifugation techniques to enrich. I haven't extracted from N.benthamiana but I have extracted lots of protein from Arabidopsis. 

Also, worst case scenario, your flag tag is cleaved or not there. Have you checked the cDNA? Good luck! 

Here's a publication example of extracting proteins that cause cell death in N.benthamiana. Maybe their protocol is different from yours. http://www.sciencedirect.com/science/article/pii/S1674205214600733 

Thank you Christian Marvelous, I have this article but I'm looking for detailed procedure/steps

Hi Mohamed,

There are four main methods, they are microarray, nCounter (NanoString), NGS and qPCR. Microarrays are good for throughput, but lack dynamic range and so accuracy, nCounter is more accurate, but lacks throughput and sensitivity at the lower end. NGS allows discovery of novel miRNAs, but is expensive and require the most data analysis. qPCR is medium price, fast, very accurate but has a medium throughput. The choice of method therefore depends on what you wish  to achieve, what sensitivity you need and how much you are wanting to pay for this.

Dear Greg, thank you very much - I´ll try one of those large polymers!

Thanks so much for your interest. I want to inform you that my sample was directely injected into ESI-QToF mass spectrometer via a syringe pump and many isotopes for each adduct were appeared. 

Hello

and look at these articles .

Good Luck

Check the APHA 'Standard Methods of Water and Wastewater'.

You most welcome

Houda

I am sorry, my research is lactic acid fermentation of fruit and vegetable.

consider then ratio of 13.8/0.38 . Multiply this value with the existing value of H. that value of H may help you in your design.

Hi Sarocha,

You might consider the following points for trouble shooting,

1. check your primers again, like do a BLAST with your gene sequence and check , check the Tm ( gradient PCR) , self complementary nature of the primers or if the primers are forming hairpin.

You should also consider the stability of the gene transcript that you are trying to quantify.

In many cases, it is possible to perform an ELISA to measure the amount of the protein thanks to the commercial availability of antibodies and sometimes whole kits.

No, it is not.  But it is to good practice to prepare the sample with mobile phase, the baseline will be cleanner.

PMA and ionomycin, at optimal concentrations, should stimulate all antigen specific cells in a mixed population. A frequently overlooked problem with these agents is that they do not function through the T cell receptor. They are able to induce cytokines previously made by anergic T cells and therefore can give a spurious readout of cytokine production from antigen-specific cells. This is explained in:  Gabrysova, L., et al. Journal of Experimental Medicine (2009) 206: 1755-1767

Yes, you can do with the help of GIS together. 

Hi, you could follow the procedure at https://www.edaq.com/wiki/Procedure_for_Capillary_Electrophoresis_with_C4D

Is this for capillary electrophoresis or capillary electrophoresis? What analytes are you trying to measure? Which instrument, CE and C4D, do you have?

Dear Sir. Concerning your issue about the detection of  metal ion via electro chemical method. The detection of metal ions has become a critical issue. Among various metal ions arsenic, cadmium, lead, mercury and chromium are considered to be highly toxic. To detect these metal ions, electrochemical biosensors with interfaces such as microorganisms, enzymes, microspheres, nanomaterials like gold, silver nanoparticles, CNTs, and metal oxides have been developed. The coupling of electrochemical techniques with nanomaterials has enhanced the sensitivity, limit of detection, and robustness of the sensors. You can prepare an EDTA-bonded conducting polymer. The modified electrode was used for the selective electrochemical analysis of various trace metal ions such as, Cu(II), Hg(II), Pb(II), Co(II), Ni(II), Fe(II), Cd(II), and Zn(II) at the different pHs by linear sweep and square wave voltammetry. The technique offers an excellent way for the selective trace determination of various heavy metal ions in a solution. Linear sweep voltammograms (LSVs) after this step were recorded for the each metal ion deposited on EDTA-CPME, which was chelated at the different pHs at the scan rate of 50mV/s. I think the following below links may help you in your analysis: