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# Math Biology - Science topic

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If I prepare a stock of undiluted platelet rich plasma from whole blood, I then would like to perform a count on a flow cytometer. I take a small sample of e.g. 20uL and flow cytometer tells me e.g. x events/uL in the 20uL sample.
Now I would like to find out how many events were in the original undiluted stock per mL.
Do I multiple my x value by 1000 to find the platelets/mL in the original stock or this is too simple of an assumption?
Hello Rh Rj,
The calculations that you listed in your comment (find the events/uL iand then multiply by 1000 to find the events/mL) are correct to convert from cells/ul to cells/ml in the original stock.
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Hello,
I would to ask if anyone knows whether the volume of buffer I resuspend a pellet in affects cell yield.
Example:
Scenario 1 : I resuspend my cell pellet in 50uL PBS (let's say there is some antibodies I am trying to incubate the cells with). Then I wash with 100uL PBS and centrifuge at a final volume of 150uL.
Scenario 2: I resuspend my cell pellet in 100uL PBS (incubate with antibodies) . Then I wash with 200ul PBS and centrifuge at a final volume of 300uL.
Scenario 3: I resuspend my cell pellet in 100uL PBS (incubate with antibodies) . Then I wash with 300ul PBS and centrifuge at a final volume of 400uL.
Scenario 4: I resuspend my cell pellet in 200ul PBS (incubate with antibodies). Then I wash with 300ul PBS and centrifuge at a final volume of 500uL.
In which scenario will I have greater cell yield and better pellet quality after centrifugation step is complete? I would like to lose as few cells as possible. I would like to know how people determine the buffer volume they resuspend their pellet in and the volume of buffer used for washes. I have noticed in multiple papers they go for higher volumes. Does higher volumes mean better cell yield ? I need to retain as many cells in the pellet as possible and lose few as possible during the wash and centrifugation. I think the volume you resuspend the pellet in and the volume in the centrifuge matters and I would ask your opinions on the best scenario to keep as many cells as possible.
Thank you.
Washing volume is much necessary for washing off the unwanted from sample.
And I found no much (remarkable) difference in washing with 300 uL to 2 mL. Increasing g force will help you reduce cells but I am not sure reducing wash volume will affect much.
Adding FBS/BSA in PBS/wash buffer (aka staining buffer) if supposed to be good as it increasing the viscosity of buffer leading to reducing mechanical forcing during centrifugation. Cells does not get stressed in staining buffer.
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I am struggling on how to calculate this. I know I need the MW, but I am not sure how these all tie together.
Il faut peser une masse de telle façon avoir n/v=1mM
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I have a 200 mg/ml ampiciline solution, how much should i add to 300 ml of LB medium to get the final concentration of 40 ug/ml?? How much bacteria (in ml) should i add to 75 ml of medium to dilute the culture 50 times??
1. C1*V1 = C2*V2
200 mg/ml * V1 = 40 ug/ml * 300 ml, or V1 = 0.06 ml (of ampiciline)
2. If you mean 75 ml is the total volume of final medium, then:
C1 * V1 = 75 ml * C1/50
V1 = 75/50 = 1.5 ml (of bacteria)
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Hello everyone,
Is it possible to calculate the E/I ratio on single-cell levels only based on miniature signals (Excitatory and inhibitory)?
If yes, then what parameters should I take to calculate it? and how?
Currently, I can get lots of parameters from mIPSPs and mEPSPs such as the number of events, decay and rise time, area, baseline, noise, half-width, 10-90 slope, etc...
Which one of these can i use to calculate the E/I balance and the calculation mathematical formula?
Thank you!
What do you think the E/I ratio is? What do you hope to show? If you wanted to, you could calculate the total charge transfer due to mIPSCs, and the total charge transfer due to mEPSCs, and take the ratio of those, but what do you think that would demonstrate? Do you think that metric would have the same information as when someone reports the time varying E/I balance due to a sensory input? Think about what the rates of minis represent, think about what the amplitudes represent. And think about whether if you did some math on these, whether they would answer the question you hope to answer.
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In the last few months, SIR-like models have been intensively used to represent the propagation dynamics of COVID-19 continuously. Even accepting that, to some degree, the different underlying hypotheses for SIR-like models are fulfilled, it has been reported that they fail to predict some relevant features of the pandemic.
Aiming to acknowledge behavioral differences between distinct populations, we proposed a multigroup SEIRA model [1]. Nevertheless, when analyzing real data from several single populations (which would force our model to behave as a SIR-like model) not all the observed dynamics could be easily represented. Aiming to solve that problem, pointing delays in reports of new cases, we proposed a methodology to reclassify them to the day where contagion was more likely to have occurred [2]. That has worked fine by now. The later was raising a question: if there were a problem with representing trends using SIR-like models, who would be to blame? SIR-like models, data-reporting protocols, or anything else?
Dear colleagues, if we consider the classic SIR model, obviously not, regardless of the fact that we do not have a closed system, there is population exchange and, most crucially, we are still not sure if all the infected remain immunized!
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SIR models are simple epidemic models, but their generalizations are used in many instances for decision making in front of crisis like the present covid-19 epidemic. A population of N individuals, at time t, is partitioned into susceptible s(t), infected i(t), and recovered r(t). This last class includes recovered, immune and dead people. A simple SIR model (differential equation) can be written as
ds(t)/dt = - b s(t) i(t)
di(t)/dt = (b s(t) - a) i(t)
dr(t)/dt = a i(t)
where a, b are positive parameters of the model.
The question is whether this model can be considered consistent taking into account that s(t), i(t), r(t) are positive and add up to N (or any constant like N=1). Are the solutions and parameters dependent on N? Is positiveness of the solutions guaranteed? Are the derivatives meaningful?
Some ideas have been also discussed here about coronavirus:
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I'm looking for a book for microarray data analysis. I'm a mathematician and I'm interested to find a book able to give a framework for microarray data analysis (from the beginning to the end-backgroung correction, normalization, dim. reduction, clustering, etc...). I found this: http://www.springer.com/gp/book/9781402072604
There are some more appropriated ?
Thanks
thanks
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I am working with an enzyme that makes 2-5 A chains from ATP. I am assuming the chain are 4 adenine residues long(no way to find out for sure; can't do mass spec). I am able to figure out how much ppi (pyrophosphate) is made when I mix enzyme with ATP... So how do I find out how much 4 residue long 2-5 A chains are being made? Using avocado's number?
Dear Nikhat, every time an adenylate is added to the chain, a pyrophosphate is released, so there must be as many pyrophosphates as there are bonds formed between nucleotides. This is summarized as follows:
2A -> A-A + PP
3A -> A-A-A + 2PP
4A -> A-A-A-A + 3PP
5A -> A-A-A-A-A + 4PP and so on
It can be generalized that the number of oligoA (oAn) chains is
oAn = Moles of PP / (moles of A - 1)
Where n is the number of A present in the oligoA chain. In your case, you want to know how many moles AAAA were formed on average, you should do the following operation:
oA4 = Moles of PP / 3
Postscript: if the oligomerizing enzyme removes all the pyrophosphates from the first adenilation step (2A -> AA + 2PP) then the correct calculation is the one signaled by Adam B Shapiro . Could you tell us if the enzyme removes PP from the first adenylate? This detail is important in small chains but practically irrelevant in very long polyA chains.
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- I am currently doing a research that mainly tests the influence of problem-based learning system on self-directed learning readiness of medical students. Two groups of medical students (PBL, nonPBL) will be identified and their SDLR will be measured. I think that unpaired t-test is most appropriate for such issue, am I right?
Also, in the same research, I am going to correlate SDLR to the academic year of the participants, and in this case, three groups (year 1, 3, 5) will be identified. I think that ANOVA and post hoc are most appropriate, am I right?
Also, I am going to correlate SDLR with academic performance (grades), but I'm not sure which is most appropriate, Pearson's r maybe?
Yes, if the distributions of your scores are all nornal, all the tests you considered are suitable.
However, as the scores of your sample may not be normally distributed nor you can assume the scores of your population are normally distributed, I would suggest you to report both the results of the parametric tests (i.e. the ones you considered) and the non-parametric tests (i.e. Mann–Whitney U, Kruskal–Wallis test and Spearman r).
If the p-values of both parametric and non-parametic tests (say, t-test and Mann–Whitney U) are on the same side (say, both <0.05), you can give a definite answer. However, if they are inconsitant, you should give a conservative answer. However, what is "the conservative answer" is sistuational, it idoes not always mean "not significant".
Anyway, you are on the right track, no need to worry at the moment.
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Do you think that the iThenticate/CrossCheck/Similarity Index would cause heavy and serious confusion in mathematics? Even destroy, ruin, damage Mathematics? Our mathematics and mathematicians should follow and inherite symbols, phrases, terminology, notions, notations in previous papers, but now we have to change these to avoid, to escape, to hide, to decrease the iThenticate/CrossCheck/Similarity Index! It’s very ridiculous for mathematics and mathematicians! Mathematics is disappearing! being damaged!
Yes! Even standard mathematical symbols and notations are captured in similarity index. The habit of using unconventional symbols and notations just to reduce similarity index is destroying the beauty and taste of mathematics.
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cell membranes control anything wants to enter or exit .
They are complex comuters which are programed to choose whatever the cell needs.
So they have to cominucate each other in different organs providing their needs.
To get accurate answers, a specific question is necessary. The question does not say if the "cells" mentioned are prokaryotic or eukaryotic, plant or animal.
And all those who are answering complicated communication methods, you should have first considered the quorum sensing in prokaryotes and Plasmodesmata / Desmosomes, which are the immediate and cytoplasmic connections in the eukaryotic cells
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Hi guys i have recently conducted a meta-analysis looking to compare 3 different drugs against each other, i am struggling to know which statistic on the meta-analysis do i use to compare the 3 drugs against each other
Am i correct in saying that you would just compare the 3 WMD in each subgroup alongside their confidence interval? i have attached a picture of my meta analysis down below
NMA, sure.
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Researchers at University of Pretoria veterinary faculty reported that mathematics was the best predictor of student performance in veterinary training.
There is much research going on to better identify this and both colleagues at Monash and Flinders Universities are involved in a consortium seeking to answer this question. The next conference related to this is in Melbourne in April:
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I saw the formula above the question, but I can't understand in detail, any one can explain in detail above the formula along with how to analysis with statistical software example using EXCEL or any other software is there pls explain...
Thanking you...
Take the length in cm and weight in gm. Find the log values of both.Draw a scatter diagram and add the trend line . Find the extreme outliers and remove it. Then insert the trend line and get the regression equation. Simple excel you can do this
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Hi,
I am currently doing my thesis and one of the experiments I did was to quantify the Fucose (a sugar) in my sample, using a colorimetric reaction.
I have already obtained the line equation, but I am not confident that I will arrive with the correct data in terms of Fucose content (mg/mL or ug/mL) in the sample.
Ultimately, I would like to know the %Fucose in the sample.
I have made and attached a file outlining all the procedures I have done and the data obtained. I humbly hope that you show me how to get the concentration of Fucose, and the percentage (%), in the sample.
Cheers,
Gene
Gene,
See attached free calibration curve software.  It is in XL format, and you can plug your values in and it will give you the curve.  You can also plug in unknown value and it will solve for the amount.  Pretty slick little program.
Besides the many valid points already made above.  Couple of things...  Since you are adding reagent to samples and standards you need to take that into account.  So the actual concentration of your stock standard 1 would be 100ug/mL x 1 mL / 5.6 mL final volume.  So your curve values will actually be 17.86 ug/mL, 8.93 ug/mL,4.46 ug/mL,2.23ug/mL  -  When I do it I get 3.73,2.42,3.40 ug/mL for trial 1,2,3.  I generated a new cal curve for each set of standards.  To determine final concentration in sample for trial 1 - (3.73 ug/mL x 80mL)/80 mg = 3.73 ug/mg x 2(diln) x2(diln) = 14.92ug/mg x 5.6mL / 1ml = 83.6 ug/mg. multiply this by the density of your sample (water = 1 mg/mL) so that would be 83.6 ug/mL.  To convert to percentage ug/mL = ppm = parts per million, percentage is parts per hundred so that's a 10,000 factor. Divide 83.6 / 10,000 would give you 0.00836%
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Hey everyone.
Has anyone read the Mol Ecol paper by Ferretti et al. 2013: Population genomics from pool sequencing? Specifically, has anyone tried to use their calculation of Fst from estimated nucleotide diversity?
For some reason, what ever I do I keep getting an Fst of zero -- even for very simple data with only two populations and very different nucleotide diversities. I have attached the equations, a description of the parameters, and my calculations to this question.
It could be that I have misread something important, or my order of operations in the summations is wrong.
For pooled data, being able to use nucleotide diversity to calculate Fst would be such a huge advantage to any study. Can anyone see what I have done wrong?
Sebastian and Xavier are right. Example: Sequences from two (haploid) populations
pop1:
ACAAAT
ACAAGT
pop2:
CCAAGT
CCAAGT
In this case, the internal variability Hs is 1/6=0.167 for pop1 and 0 for pop2. However, the average pairwise nucleotide diversity between the populations is 0.25 because of the fixed difference A/C between populations in the first base.
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Hello,
I need urgent help to calculate absolute values of standard deviations from surface & volume calculations of cylindrical shapes. The tricky part is to calculate according to the law of error propagation:
The 2 formulas are the following:
lenght of mantle m = root[(R-r)2 + h2]
mantle surface M = (R+r)* Pi* m
measurement values are: r=0,89 R=1,43 h=27,  as well as Dr=0,79 DR=0,08 Dh=0,5
Can somebody help me out with the exact formula for the standard deviation of the measures?
Thanks for help! Verena Hoelzer
You first calculate the partial derivatives of the two fromulas for each measured variable. Then you calculate the sum of the products of the errors (Dr, DR, and dh) with the squared corresponding partial derivative.
Example for the length of the mantle:
dm/dR = (R-r)/root(w)
dm/dr = -(R-r)/root(w)
dm/dh = h/root(w)
where w = (R-r)²+h². The squared derivatives are
(dm/dR)² = (R-r)²/w
(dm/dr)² = (R-r)²/w
(dm/dh)² = h²/w
The propagated error is
Dm = DR*(dm/dR)² + Dr*(dm/dr)² + Dh*(dm/dh)²
Dm = DR*((R-r)²/w) + Dr*((R-r)²/w) + Dh*(h²/w)
Dm = (DR+Dr)*((R-r)²/w) + Dh*(h²/w)
I hope I did not make some error somewhere.
You will surely be able to do the same for the simpler formula of the surface.
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I am working on food toxicology. I am about to get a set of results on the toxicity from various substances in terms of ug per ml or ug per mg. with these data, can I derive a risk index for food substances? if so, how?
For some types of toxicity there might not be data available on the effect of the substances in food.   It depends on what your toxicity data is, for example MIC or mutagenesis, or some other thing.  The principle is that if a substance is proven to be toxic at a (usually high) concentration, then how toxic is it at lower concentrations, such as the concentrations that might be found in food?  Sometimes it involves guesswork and speculation to make a claim that a substance might be toxic at small concentrations.   There is a lot of industrial data on the toxicity of chemicals but scaling this down to food-level concentrations and coming up with a toxicity index may involve precautions only, and not hard evidence.   An example is pesticides: at high concentrations it kills insects (and birds and humans for that matter), but how low a concentration is considered 'safe' ?    If that concentration kills cells in a petri dish, or causes birth defects, or cancer in animals, then I would not consider it safe.  I believe the US FDA (or one of those federal agencies) has general guidelines on this.  Diluting it down from the lowest "known" toxicity level for human consumption, for example one-tenth of the known lowest toxicity concentration, might be deemed "safe" for human consumption.  You would have to look into that.
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The Golden Ratio is generally used in mathematics and arts science based on Fibonacci number. Definitely we have used this technique after the great mathematician, i.e. nature. You can see that nature has used this everywhere like in sunflower, stem and branch arrangement in plant, petals and most importantly the DNA. DNA molecule 21 A width and 34 A length are two Fibonacci number obeying this rule. Also the minor and major groove of the DNA shows similar results.Taking account of DNA chromosomal arrangement there should be a mathematical view of genome function, i.e. DNA replication and repair to gene expression and mutation, breaking, joining of DNA double strand or single strand,,RNA and proteins structure, etc.
25 years evidencing golden ratio in DNA genes chromosomes and genomes:
Bienvenue, Welcome Bienvenido …
in my world of « NUMBERS in DNA and GENOMES » ...
Three strong publications overview:
Codon Populations in Single-stranded Whole ... - The Shakedown
Codon Populations in Single-stranded Whole Human Genome DNA. Are Fractal and Fine-tuned by the Golden Ratio 1.618. Jean
Integers neural network systems (INNS) using resonance ... - IEEE
Dr Jean-claude perez
Decoding non-coding Dna Codes: Human Genome Meta-Chromosomes ...
Ref 2: Jean-claude Perez, L'ADN DECRYPTE (DNA DECODED), (1997) Marco Pietteur publishing (Resurgence collection) Embourg Belgium, ISBN 2-87211-017-8 (in french)
others :
here are some scientific articles related 25 years research on NUMBERS in DNA and GENOMES...:
… after a pioneering in Artificial Intelligence (artificial neural networks, artificial vision and Robotics) in IBM research :
1988 and 1989 : 1st book : « De Nouvelles voies vers l'intelligence artificielle : Pluri-disciplinarité, auto-organisation, réseaux neuronaux » (1988 and 1989) :
1990 : 2nd book: « La revolution des ordinateurs neuronaux » (1990) :http://www.amazon.fr/Révolution-ordinateurs-neuronaux-Jean-Claude-Perez/dp/2866012038
Now, here are some scientific articles related 25 years research on NUMBERS in DNA and GENOMES...
1/ 1991 : Article, first publication on NUMERICAL STRUCTURE of DNA :
and
2/ 1994 : C.D. « The 1st Music of Genes » :
3/ 1997 :3th book : “PLANèTE TRANSGENIQUE” (1997) :
4/ 1997 :4th book : “L'ADN décrypté” (1997) :
5/ 2001 : Annecy (france) Fondation Marcel MERIEUX, conference on PRION protein :
6/ 2009 : 5th book : “CODEX BIOGENESIS” (2009) :
7/ 2010 : « Interdisciplinary Science » (springer verlag) article :
8/ 2011 article Beijing conference (china):
9/ 2011 Montevideo conference Universitad de la republica (uruguay):
10/ 2012 : article in « The cerrebellum handbook » (springer verlag), published with researchers from Silicon Valley (USA) :
11/ 2013 : article in « Applied Mathematics , Biomathematics issue » :
Artificial Intelligence :
25 years ago, our artificial neural net “FRACTAL CHAOS” provides “déja vu” holographic like memory from a small 3×3 fractal chaos neurons: more in:
and
and
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I am interested in developing  a mathematical model for biopolymer coated fertilizers using a multiscale modelling approach. I would be greatful if anybody could guide me more on this.
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I have a dataset with 48 sites, 6 site co-variates, 5 sample covariates modelled for 5 occasions. I think I can fit up to at least 20 parameters. Whenever I run a base model psi(.)p(.) I get a estimates with SE. However, when I increase parameters estimates go haywire (infinity etc). Even running a correlated detection base model I get the same, does anyone have a fix for this in PRESENCE or is it that the data is bad?
A couple thoughts....
The number of parameters you can reliably estimate will also depend on the true occupancy and detection probability. How many sites do you actually detect animals? At those sites, how many of the replicates are they detected in? If the answer is few, you could still have a low sample size issue.
I would build on the base model gradually by adding one parameter at a time and running the model to see when the fitting starts to fail.
I would also run a model fit test on the models that do run to see if you are meeting the basic assumptions of the model. Poor model fit could be causing your issue. If there is poor model fit you might consider a model that models the detection intercept as a random effect across sites. Or you might consider a model that evaluates non-independence among individuals or detection occasions.
Good luck,
Dan
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I have a set of continuous data that present a very strange distribution with multiple symmetrical peaks at the left and right of a very sharp mean value.
Can someone help me by suggesting how to fit Thomae distribution (see paper attached) to my data in order to check the hypothesis of a discrete sampling which is only apparently continuous?
In addition to what has already been suggested, you may find the Manipulate function from Mathematica helpful.    For a detailed explanation and a number of examples, see
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What sorts of properties of soil and plant are effective?
thank you so much Dr. Noori, it was very useful.
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Like this: x+y=0 and x+z =1.
Dear Ahmed,
As soon as you have a system of two equations in three variables, you would have an infinite number of solutions. Therefore, you can of course solve the system of equations, although there will be no unique solution. For example, in your case, you have x + y = 0 and x + z = 1. Accordingly, you have to fix one of the three variables, and solve for the other two. For example, x = 0 would give y = 0 and z = 1.
x can take an uncountably infinite number of values, and therefore for every x, you would ultimately get an uncountably infinite number of values of y and z.
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