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The problem: We are interested in how ketamine modulates hedonic experiences such as chills. Participants listen to music while they are in the scanner both on ketamine and placebo. Participants also pre-rate all the songs outside the scanner a week before. For our analysis we need to know when participants experience their peak emotional moment during the scan session.
Our ‘solutions’ that did not work:  1. Live rating – people press a button while they experience their peak moment. Problem: we confound our neuronal signal of interest (pleasure) with motor activity. 2. Additionally rating the music after the scan session. Problem: even though, we think that the peak for most songs won’t shift between the baseline rating and the in-scanner rating sessions, it might do so during the ketamine condition (makes everything more pleasurable and maybe even earlier). So, if they rate the music again afterwards when most of ketamine's effects have already subsided, we might not find the same peak moments as during the scan session. (plus, we also doubt that participants could reliably remember when those peaks occurred during the scan session…)
3. measure physiological responses. problem: yes, skin conductance does correlate with chills but we do not have the equipment for that...
Does anyone of you have an idea how we could measure the peak moment during the scan session without majorly confounding our measurements? I would really appreciate your help!
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Sorry, for the long delay!
Our music excerpts are 70 seconds long and we have 20 songs altogether (10 neutral/10 positive).
Would you mind sharing this groove literature with me? I would be really interested in reading papers showing this connection between moderate music complexity and peak moments!
ad 1. Yes, we thought about that but many scientist I talked to argued, that the brain's network activity is so complex and sophisticated that one cannot (should not) simply subtract this confound from the signal... But I will look into it again! Thank you!
ad 4. Mhm this is a very good idea! I will look into that aswell.
Thank you for your responses Connor!
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I think I understand now that you ignore the baseline (pre-test) data when calculating Hedge's g. Instead subtracting the intervention posttreatment mean from the control posttreatment mean and diving the result by the pooled standard deviation (of both samples at posttreatment). However, I am now wondering that means for studies where, despite randomisation, there were significant differences in the outcome of interest at baseline.
For example, with the data below, where let's say the mean value (M) is pertaining to a depression score. Would it be appropriate to calculate Hedge's g in the method above or what would have to be done differently, if intervention and control group baselines scores were not similar?
Thankfully I think only a couple studies had this problem, but I am unsure whether I exclude, perform a correction, or run as normal in the meta-analysis.
Intervention Group Pre-treatment: M=63.92; SD=10.67; N=63
Intervention Group Post-treatment: M=59.43; SD=7.23; N=63
Control Group Pre-treatment: M=74.57; SD=9.79; N=65
Control Group Post-treatment: M=72.69; SD=4.84; N=65
Many thanks for any help.
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Dear John,
Please, read my paper and see some options:
Regards,
Gokhan
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In meta-analysis of some studies, a study didn't provide change from baseline results, so I have to calculate it, I only have the following data: pre-treatment mean&SD, post-treatment mean&SD. it is easy to get mean difference by subtraction, but I can't calculate SD!!
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Dear Mohamed,
I am late to write, but this can be helpful for the people newly experiencing the same problem. For a solution, please read my paper's methodology:
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I am trying to calculate the change in standard deviation for my metaanalysis and would like to know the correct way of calculating it.
I have the following data available:
1. mean for control group at baseline and endpoint
2. mean for intervention group at baseline and endpoint
3. 95% Confidence interval for control group at baseline and endpoint
4. 95% Confidence interval for intervention group at baseline and endpoint
5. Number of subjects in control and intervention group
I would like to calculate the difference in standard deviation for control group (sd_baseline and sd_endpoint) and intervention group (sd_baseline and sd_endpoint).
I would like to use the cochrane handbook as reference:
It is stated that
"When there is not enough information available to calculate the standard deviations for the changes, they can be imputed."
Does this mean we need to impute the correlation coefficient for all the studies for every different outcomes separately?
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Dear Ambrin,
I am late to write, but this can be helpful for the people newly experiencing the same problem. For a solution, please read my paper's methodology:
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Dear Research Gate community,
I’m conducting a study on how different management practices in wetland (ponds) affects the diversity and abundance of species in the wetlands. The different ponds are next to each other. Some ponds are control without any management practices. The water in the treatment ponds is regularly drawn down and filled up again with water from the river. We recorded the waterbird species number and abundance of each pond regularly (record the bird data of all the ponds at the same time for each survey).
- In the first year, we conducted a baseline study in which no treatment was done for all the ponds (data were collected monthly).
- In the second year, we conducted the treatment (operational study), and data of birds were collected weekly.
We’re now trying to study:
1) first, if there is any difference between the treatment ponds and control ponds during the operation
2) if there is any difference between baseline study and operational study of the same pond.
We wonder what kind of statistics are suitable for statistically analysing our data.
Some problems we are encountering is:
1. The data do look like normally distributed. The data collected are time series data, there are natural seasonal variation in the number of waterbirds in our region (a lot of migratory birds in fall and winter). How to take into account of influence of the time of survey.
2. The sample frequency for baseline year (12 times) and operational year is different (52 times), how to compare the difference between baseline and operational year.
Highly appreciate any help or suggestion!
Best regards
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Try to check a book on: Design and Analysis of Experiments, just be sure that you are doing the right design. Regards.
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When is the specific time should I measure the Baseline BGL? How about the Pretest and Post-test Blood Glucose Test in Oral Glucose Tolerance Test? I want to conduct a 4-hour fast to white mice.
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If you want measure fasting glucose, you must tak it beFore your mice get food and they not eat 8 hours. If you want measure prandial Glucose you must take 2 hours after meal. You can make it for baseline bolos glucose level
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Hi,
I was wondering if anyone has experience statistically analyzing Ca2+ oscillation patterns?
Specifically, models you used to quantify what you considered to be a "peak" in the oscillation pattern, how you determined baseline and what programs you used to accomplish this.
Thank you!
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You may like this publication of the topic:
doi: 10.1096/fj.201801460R
Best, Karoly
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I am looking to do a meta analysis on intervention RCTs but all of the papers have provided baseline and post intervention Mean (SD) for the groups. I have looked at the Cochrane page as I am using Revman ( https://handbook-5-1.cochrane.org/index.htm#chapter_16/16_1_3_2_imputing_standard_deviations_for_changes_from_baseline.htm )
However, I am not from a statistical background so this is getting quite complex. Just wanted to see if there are any resources to guide as this must be very common, and I would hope there are fairly simple ways to deal with it. I have come across using the SD from baseline or post-intervention as the SD for the change but obviously am hesitant of just going along with this.
As only baseline and post-intervention Mean and SD are reported for the majority of the studies, I am thinking I may need to leave it at the narrative synthesis and leave out the meta analysis. But given that the trials are all randomised with similar baseline characteristics and biomarker parameters, could I just enter the final measurements in both groups, rather than the mean change and SD from baseline?
Appreciate of any input.
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Piyush,
Please read the methodology of my paper:
you will see what you can do.
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I am working with a 7820A GC and 5977B MS. Recently, we changed the helium tank and since then we have been seeing baseline noise in the chromatogram which is high enough to interfere with my peaks. We also found that the N2 and O2 counts have been higher than we used to see; 5-8% and 1-1.8% respectively. Previously, the counts used to be 1-2% and <1%
We replaced the helium regulator, purged the inlet, changed the septum, baked out the MS and the baseline noise still exists. Any ideas why this might be happening?
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Perhaps you have found the answer, but apparently the Helium cylinder can be contaminated with N2 which explains the high N2 ratio.
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Hopefully someone can help me out with how to quantify a significant representation of a group.
I am analysing a bias in protein detections. To see a pattern I group the proteins in their families I know the baseline occurrence of a family, based on the proteome. The sample data consist of a list of how many times a protein family occurred in my sample (always equal or less than the baseline).
Data -- baseline ---- %
1 -------- 6 --------- 16.67%
2 -------- 15 ------ 13.33%
11 ------ 141 ----- 7.80%
3 -------- 18 ------- 16.67%
58 ------ 361 ----- 16.07%
1 -------- 3 -------- 33.33%
1 -------- 21 ------- 4.76%
7 -------- 421 ----- 1.66%
1 -------- 2 ---------- 50.00%
I could take a percentage of representation, but since the families are not equally represented this creates a bias.
Here the value of 58 out of 361 weights more than 1 of 2.
Is there a way to calculate the 'magnitude' of representation beyond percentage to take the frequency into account?
Chi-square testing doesn't work since the dataset consists out of more than 600 family groups.
I struggle a bit to get my problem forward, please ask if I can do anything to clarify.
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Perhaps I am being dense here, but percentage does take frequency into account,
%A = ( freqA÷total ) ×100
Am I missing something here.?
Best wishes David Booth
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Hello,
I am hoping that someone who is well versed in statistics can help me with my analysis and design. I am investigating the torque produced via stimulation from different quadriceps muscles. I have two groups (INJ & CON), three muscles (VM, RF, VL), three timepoints (Pre, Post, 48H) in which torque is measured at two different frequencies (20 & 80 Hz). In addition to the torque, we also want to look at the relative change from baseline for immediately Post and 48H in order to remove some of the baseline variability between muscles or subjects. A ratio of 1.0 indicates same torque values post and Pre. This is a complex design so I have a few questions.
If I wanted to use repeated measures ANOVA, I have to first for normality. When I run the normality test on the raw data in SPSS, I have one condition that fails and others that are close (p < 0.1). When I run the ratios I also have a condition that fails normality. Does this mean now that I have to do a non-parametric test for each? If so, which one? I am having a difficult time finding a non-parametric test that can account for all my independent variables. Friedman's is repeated measures but it is not going to be able to account for group/frequency/muscle differences like an ANOVA would.
Is repeated measures ANOVA robust enough to account for this? If so, should I set this up as a four-way repeated measures ANOVA? It seems like I am really increasing my risk of type I error. It could be separated it by frequency (20 and 80 Hz) because it's established a higher frequency produces higher torque but as you can tell I have a lot of uncertainties in the design. I apologize if I am leaving out vital information in order to get answers. Please let me know and I can elaborate further.
Thank you,
Chris
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You can use a plain ANOVA for repeated measures (time) according to Bhogaraju Anand suggestion, as for normality test, forget it...it is not essential and parametric tests are sufficiently robust as for deviations from normality (see attached file)
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As per literature,
"The net peak heights were determined by subtracting the height of the baseline directly from the total peak height. The same baseline was taken for each peak before and after exposure to UV.
The carbonyl index was calculated as: carbonyl index = IC/IR(100),
where IC represents the intensity of the carbonyl peak and IR is the intensity of the reference band."
Now, how do I substract the baseline height from the peak height???
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Dear Gitashree Gogoi,
I suggest you to calculate carbonyl index according to the method explained in the attached article. I hope it would be useful.
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Edit: the paper was approved so if you want to see it just message me :)
I'm writing a paper on a multimodal active sham device for placebo interventions with electrostimulators. We believe it has a low manufacturing cost, but it's probably better to have some baseline for comparison. Have any of you ever requested a manufacturer to produce a sham replica of an electrostimulator to be used on blind trials? If so, how much did it cost? Was it an easy procedure?
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I would say, if you need it for a study purpose not for exhibition (just kidding), I would suggest to check if it is possible to use the original working device with just unplugged wires in a sham group. Just and idea, good luck with your paper!
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What is the origin of the shift (up) in the baseline of the UV-VIS spectrum as noticed from 300 nm to 800 nm in the screenshot attached? I'm measuring phenobarbital in 0.2 NaOH against 0.2 NaOH blank. I have tried turning off fluorescent lights, CRT monitors, and capping the cuvette while measuring the sample on my HP 8453 chemstation.
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Baseline drift is a common problem in UV-vis spectroscopy - see https://ibsen.com/resources/detector-resources/subtracting-dark-spectra/ for a useful outline of the issue. I am glad that you were able to pinpoint the source of error in your experiment, Zaid Assaf. Increases in dark current (e.g. due to heating) seem to be a frequent offender, though loss of minor absorbing species (e.g. consumption of acid during a reaction) or changes in the solvent environment (e.g. in gradient chromatography) could also contribute.
My advice to William Letter is to read questions carefully before jumping to conclusions regarding the poster's knowledge and experience level. Condescending comments like "This is basic spectrophotometry" are not constructive and may deter other researchers from seeking assistance online. In this instance, you mistook the poster's issue with baseline drift as a misunderstanding of the Beer-Lambert law, despite repeated clarification. I hope that you will take greater care in your future replies.
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I use BV2 cell line to record a calcium signal with a dye called Calbryte 520 AM. I added ATP into the perfusion system at 5 min, and I could see a peak in the figure. I did four times of this experiment, but the calcium baseline decreased continuously. Generally, the calcium baseline should be stable.
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Nice signal. The decrease is probably due to bleaching like both answers said. Continuous and more exponential than linear. Nothing dramatic you can either decrease the illumination power to reduce it or you can detrend it post hoc with your software or choice. The fact that it comes back up on the blue trace is a bit strange but probably due to the way you calculate your DeltaF/F0.
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Hi
I am developing a method for computing fussy similarity in WorDnet. Previous work mainly focused on the simialrity of SynSets (concepts).
I am serarching for a snatdard baseline for reasons of comparison. My question is: what is the standard baseline for comuting the similarity of words in wordnet.
Thank you.
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You might also try to take a look at this article:
We used a little bit more sophisticated approach than the one Olga Seminck indicated. However, I would simply try to combine both approaches.
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I want to find each peak's enthalpy change (area under the curve). Can I make the baseline as in the photos below?
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The question is about the baseline as he has shown attached with @Ong Hui Ling question. My answer is not to use such base and I have given a suggestion above.
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Over the last couple days, my colleagues recently noticed a significant amount of baseline drift in their chromatograms from one HPLC (see attached). Across columns, methods and samples it seems to have a consistent type of baseline shift. I've monitored the pressure and it is what I expect it to be/stable. Additionally, the washes (see attached) have a sharp increase, plateau and then drop in all of them. Just a day or two prior, the baselines were perfectly fine. I am not sure what is causing this sudden issue nor how to resolve it. Please advise. Thank you.
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Also, I see that you are running at 210nm. Such a low wavelength will show just about every change in the system (normal, as 210nm is not selective for anything and will show most "changes"). Additionally, you have the software "Reference Wavelength' feature turned ON. Please turn that feature OFF and read the article below for more info why it so important to not have it ON and have professional training in HPLC operation (esp DAD) before you run samples.
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I am conducting a meta-analysis of continuous data using RevMan 5.4.
Included studies express their results as mean, SD at baseline and end of study for intervention and control arms. With these data I can impute change from baseline which I will use to perform a meta-analysis of change scores.
However, in a few studies, due to patients lost to follow-up, the number of patients at the end of the trial is lower than at baseline. RevMan 5.4 requires mean change from baseline, SD of change and sample size to perform the meta-analysis. Which number of patients should I use to perform the meta-analysis (the sample size at baseline or the follow-up)? Or would it be better to exclude these studies?
Thanks in advance for your help.
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Studies with bigger sample sizes should have higher weights, that's the only logical thought I can imagine here.
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This is what the baseline looks like running 30% acetonitrile/70% water, using a UV/VIS detector set at 195 nm. I thought it might be air bubbles, but running 100% water gives a perfectly flat baseline. I degassed the mobile phases, and primed the lines several times. I also ran isopropanol through the system for a while, but that didn't help. The pressure of the system is consistent (around 750 PSI), and I cannot find any leaks. Could this be a solvent mixing issue? I know that acetonitrile absorbs at this wavelength, but I've never seen it cause this sort of issue. I'd really appreciate it if anyone could provide some suggestions, thank you!
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A few comments:
  • Running at 195nm is NOT advisable. EVERYTHING will absorb at that wavelength providing NO selectivity for the sample under analysis. Consider a different detection method that provides selectivity.
  • Your chromatogram shows ~ 0.5 AU of signal and noise. The detector is not balanced and the mobile phase is NOT uniform in composition.
  • The cyclical peak shapes implies a few possible causes: Poor quality degassing (the degasser may be broken) of the mobile phase resulting in pump cavitation and/or sticking check valve(s). *Running just one solvent alone usually gives a good result and the problem re-appears when mixing the two; the water and/or ACN are not pure (NOT HPLC grade); their is a problem in one of the mixing channels of your system (If it is a mixing problem, and it may or may not be, TRY PRE-MIXING THE TWO LIQUIDS together in one bottle, then run the liquid through one channel and monitor the signal output channel. Is the baseline flat? Does this fix the problem? If so, you having a mixing problem. If not, it might be due to large amounts of impurities in one or both of the liquids used.
  • Most importantly of all, you appear to be using HPLC without any formal training. It takes the average scientist 5 years of professional work just to achieve a basic level of skill in HPLC. Please have someone trained in HPLC locally help you. This technique is best utilized with the help of an experienced chromatographer.
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I'm looking for a software able to process HPLC-UV chromatograms (espacially baseline correction and peak alignement) in order to integrate the data to statistical analysis.
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You could also try the package I am developing in R: https://github.com/ethanbass/chromatographR. It doesn't have a parser for chromeleon files, but I could probably write one for you if it's really just a text file?
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Dear colleagues
I have a question regarding HPLC baseline issues. Please see attached file and your suggestions would be greatly appreciated.
Xiao
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As it appears you are using HPLC for the first time and it takes many years of professional experience just to achieve a basic level of experience with HPLC, please contact someone local at your school to help you. As a student, you should not being doing this alone. Having an experienced chromatographer to help you on-site setup a method and run the analysis will help you be successful. The manual that came with your HPLC column also has a lot of useful information regarding the use, cleaning/washing/regeneration/storage too (do not store it in water !!!!) . Be sure to contact Bio-rad if you have column questions.
Also, the fact that your column temperature is set to 80C and your RID at 55C, will result in a slow temperature gradient forming. This may cause long term baseline drift. Since temperature is used as variable in method development (for your specific application) and temperature changes take a VERY LONG TIME to equilibrate, on column, be sure to insulate all capillary lines leading from the column to the detector (see article linked below) and please allow for LONG equilibration times.
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Hello,
Recently I have been getting an extremely unstable baseline throughout the runs I have been performing. I allow the HPLC to calibrate for about an hour by just running mobile phase at the flow rate we use for our method. I also clear the RID detector by running the mobile phase at the same flow rate through the RID channel by opening it using the software. The UV baseline is also unstable.
I have also a noticed a problem where the elution times vary greatly between samples within the same sequence. The difference can be as high as 7 minutes. I switched columns and am still receiving the same problems.
Does anyone know how to fix these issues?
Flow rate - 0.6 ml / minute
mobile phase - 5 mm H2S04
Column and RID Temperature - 55 C
Our samples are mixtures of sugars diluted 1:5 in 16 mm NaOH.
I attached an image of what the peaks look like.
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Hi Chris
I am facing similar issues of baseline stability and varying retention times. what I understand is RID is highly sensitive to temp and it takes a good amount of time to stabilise the baseline. I am operating at 40 degress both column oven and detector.
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N/A
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"What test would offer insight as to group x condition?"
Only a parametric model can do this. As soon as use ranks (i.e. some kind of "non-parametric analysis") an interaction is not meaningfully interpretable.
There are more important things to consider when analysing an interaction: it makes a difference if you assume that effects are additive or multiplicative. A meaningful interpration also requires that that the observed interaction is not due to ceiling or floor effects.
It's easy to do "some test" and to get "some result", but it is tricky to get a meaningful interpretation. I suggest to collaborate with a statistician.
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Hello everyone,
i have a question regarding event/task-related EEG data. Are there any indications that baseline power increases from trial to trial? For example, in the case of a fine motor task (e.g. finger movement), that baseline power slowly accumulates from trial to trial through the task (baseline power trial 1 < trial 100)?
I am aware that the time between trials should be such that there is a return to baseline. However, if the power increases slowly, could it be that an effect is only seen after 70-80 trials?
Does anyone have experience or know of studies that can provide guidance on this issue?
Regards
Niko
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Given that the EEG signal is non-stationary, I'd say that it is perfectly possible for the signal to 'drift' across trials. In fact, I have at some point recorded data with such a behaviour. In any case, there are drifts that can be removed with the appropriate type of filtering (see, for example, this link: https://benediktehinger.de/blog/science/electrode-drift-in-eeg/). I would recommend normalizing your task data by the power at baseline, so that, if such a drift is indeed present, you can consider it in both task and baseline.
You could also always compute the baseline power and check if it, indeed, increases over time between your trials.
Does that make sense?
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I prepared an article that resulted a baseline aspect. But I do not understand, in what types fields, in where I submit the article in marine pollution bulletin (Baseline and normal). Moreover i have two article in review in baseline study. Can i submit again a paper in baseline? Has any quality deviation regarding two kind of publishing (Baseline and normal) in marine pollution bulletin. Actually i want to submit the article as a corresponding author. want a good suggestion.thank you for advance.
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"The objective of BASELINE is to publish short communications on different aspects of pollution of the marine environment. Only those
papers which clearly identify the quality of the data will be considered for publication. Contributors to Baseline should refer to
‘Baseline—The New Format and Content’ (Mar. Pollut. Bull. 60, 1–2)." ( ).
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Hello. I am using a reversed-phase HPLC with C18 ODS column with PDA detector. My mobile phase is composed of 15mM CH3COONa, 6% V/V CH3CN (ph-5.5) . The baseline starts from Zero and gradually drops to minus values and never stabilizes, I have ran multiple washes with Methanol: water (50:50) but still facing the same problem. I was wondering what could be the reason for this drop??
Thank you in advance!
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Typically, a drop in absorbance is due to the change in RI or UV spectrum particularly between the mobile phase and extraction solvent and is apparent in the solvent front. This is different. I would ignore it because your sensitivity is set too high. I would be concerned if it was ~0.1 mAU or 10X higher than what you have in your chromatogram. Your molecule of interest will be at least 100 mAU or 50,000-100,000 peak area units.
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Hallo
I am conducting an intervention study where I have two groups (control and experimental ). The study subjects were monitored at baseline and then at endline of the study. I would want to;-
1. Compare the data at baseline between the two groups
2. Compare the data at endline between the two groups
3. Show the effect of the intervention on the parameters of the study subjects
4. Check for differences within the groups eg control at baseline compared to control at endline
Kindly advise on the appropriate statistical tests I should perform
Thanks
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Jane Mbijiwe Because you are working with the same population and the same treatment/intervention, try utilizing the Wilcoxon Rank Sum Test. It will assist you in getting to know the group that benefited the most from the intervention both before and after.
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Live cell imaging using IncucyteZoom is the best way to measure effect of drugs on rate of cell proliferation. I noticed that despite of how accurate we try to count the cells using automated cell counters (countess) sometimes not all the datapoint do not start at the same point.
If we normalise the data to timepjoint 0 the gradient of the slopes changes which is not ideal.
One researcher stated I should seed cells at different densities and introduce an error in counting and choose the densities that start at the same point which I believe is not correct because you are just increasing additional errors to that of countess and additionally assuming all cells have the same size and shape.
I had been deducting the baseline values of time-point 0 from all time points of respective condition such that all points start at 0. Reason: This deduction will not alter any slopes.
The only problem is when cells reach 100% confluence and plateau deduction of baseline for example: if the confluence started at 20% at 0 time point and reached 100% at 96 h and remains plateau unto day 7 , deducting the value of baseline will show that the cells reached plateau at 80% at 96 h and 120 h. If I plot the data only in the log phase and deduct the baseline I think is the best option.
Can anyone please comment on this and help me out.
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David J Walker is it possible to cite the publication in which you found informations about normalisation please
Thanks
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Hi all
I am having trouble getting LTP in hippocampal slices (CA3-CA1) from 9-16-week-old WT mice.
I can get LTP fine in aCSF containing 1uM gabazine (I cut CA3 to prevent epileptic activity) but in plain aCSF I don't see any LTP. When I record using Gabazine I often see spiking after LTP induction and I'm worried these are skewing my data. So it would be good if i could record LTP without using Gabazine to prevent this.
In both conditions I get stable baselines and the slices look healthy. For the baseline I use 40% of the max response. I extract in choline chloride aCSF (i have also tried slicing in aCSF containing sucrose). After slicing, the slices are maintained in standard aCSF and left to rest for 1 hour before being transferred to the rig at 30 degrees. I induce LTP using a theta burst stimulus.
Please can anyone help explain why I cannot get LTP without Gabazine? And please let me know if you have any suggestions of what I could try to get LTP.
Thanks in advance
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Hi Lauren, for me, for some reason, LTP is inducible only in like 20% of slices at 50% of amplitude at first PS response. But if instead, I take ~60-70% of this amplitude, the success rate increases to 75% (with no PS at during LTP recording).
And also I think it is better to deepen the stim much deeper (like penetrate it >half of a slice), so more fibers would be stimulated. But I didn't try it yet.
By the way, since I'm answering pretty late and have similar problems (wish to have 100% success rate at 50% stim of max), could you please tell, did you solve the issue? If so, how?
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Hello Everyone,
I am having trouble with baseline removal when creating an epoch containing 1 trial. After creating the epoch, a window pops up that reads "remove mean of each data channel", rather than the window that allows you to specify the time window for baseline removal. I am also unable to later remove baseline by going to "tools" --> "remove baseline".  How may I baseline correct this segment using a window of -200 to 0 (rather than the default -1000 to 0)? Is there a way to change the default settings of the baseline removal in eeglab?
Thank you!
Best,
Ariel
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Thank you Joao and Nik for your replies.
I was able to baseline correct from the command line/Matlab space, as you suggested Nik. I used the following code, which saves a new dataset with the applied baseline correction:
pop_saveset(pop_rmbase(EEG, [-200, 0]), 'filepath', 'C:\Users\s15aa\Downloads\eeglab_current\eeglab2021.1\EEG Epochs and Baseline Correction\PSVTR08_hard_baseline_corrected')
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the input data if between 2006 and 2020, and the number of years I choosed is 100 years. So the generated data gives 100 years values from year 1 to year 100 without mentionning any dates.
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Hi dear Sara Guemar,
The first and most important thing is that lars works as a stochastic tool. Therefore, it is important that you understand how the LARS works.
Firstly, I want to emphasize a key point. You mentioned that you chose 2006-2010 as your baseline. If you are going to publish your research as a scientific article, you should choose the standard period of 1980-2010. Lars is calibrated for the period.
Secondly, you asked what is the start of the period selected for 100 years? After choosing a GCM model and scenario, you must select a period. When the selected period is, for example, 2041-2060, it is not important whether you choose 100 years or 10 years; the model will generate data for that period. My description of it is as follows.
As I told you lars is a stochastic weather generator. It means the output of Lars is not a time series. This means that the numbers for 2042 are not after 2041. Thus, averages (or std) of series can only be used for analysis. LARS is not a prediction tool, but a projection tool.
You can test this out for yourself. For the period 2041-2060, generate data once for 20 years and once for 100 years. Then compare the average of the temperature and precipitation for the two series. You will see that the means are very close to a very good approximation.
Finally, you should know that you are allowed to analyze the results on a monthly basis since Lars does not support daily changes unless you manually create scenarios based on daily GCMs.
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Hello,
I was wondering what are some more concrete examples of baseline imbalances? Thanks
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To get a comprehensive understanding, first an idea about baseline characteristics is compulsory. These characteristics are the medical, demographic and prognostic variables in Clinical trials. When an error occurs in creating intervention groups in baseline characteristics when participants are non corresponding to prognosis, this is called Baseline imbalance.
Hope that it helps.
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Hi everyone,
I hope you have had a great day so far!
Well, I wonder how I can run a mixed-effect analysis on Stata with the following features:
Research question: What baseline variables predict my dependent variable over time?
Dependent variable: discrete --> Poisson distribution
Independent variables: both categorical and continuous variables
The following model is what I have planned so far. But I don't know how to consider only the baseline data from my IDs.
xtmepoisson DV ID##time || participant_ID:time, irr
My question is: What do I need to do to consider only the baseline data from my IVs?
Thank you in advance and happy holidays!
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Dear Lisandra Almeida,
If I would be your help, my email: miregech897@gmail.com.
Good luck!
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I am investigating the efficacy of Gestalt therapy with adolescents engaging in self-harm, using a single case experimental design. I have administered some tools to measure the level of self-harm, anxiety and depression at baseline, after 15 sessions and after 30 sessions. What statistical measures would you suggest I use to show the effect of the treatment besides visual analysis?
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Paired sample t-tests could be a simple approach to look at differences in a single variable from pre treatment to post treatment.
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Hi there, my research aim is to reduce latency in fog environment, and I have a baseline that I would like to compare my work to, in the baseline research paper, their proposed method was compared to a method called "no offloading" and they saved the latency by 40%. in my work, I compared my proposed method to the same method "no offloading" and I saved latency by 80%. the question is that do I have to do coding for the baseline (in the simulation) to officially comparing my work to?? the problem is that the baseline method consider different factors that I don't consider such as deadline, and the values of the parameter used in the baseline is different from mine.
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Yes, you can compare your findings with someone else's findings to the same research question, even though you used different methods. Findings should be objective, not method dependent. Using the same method is a test of their replicability.
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Can I use the means difference (difference in mean between endpoint and baseline) and associated standard error to calculated a standard mean difference (SMD) and its 95%CI (I also have an exact p value and sample size)?
for context: it's for a meta analysis all of my other studies provided me with the means and SDs needed to get my SMD.
n=239, mean difference: −1.2 [SE 1.48]; p=0.4154
either formulas or online calculators or references are welcome :)
Thank you
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OK, I assume you have baseline and endpoint measurements for each of the 239 subjects. I was lazy and used the Comprehensive Meta-Analysis (CMA) software to do the calculations for me. There are two possible ways you might calculate the standardized mean difference (SMD). The first one (see Test1.jpg below) is probably the best procedure. It makes use of your paired t-test p-value. You do not have to make assumptions about the correlation between the baseline and endpoint values like I used in Test2.jpg. In Test2.jpg (rows 2 and 3), I used assumptions of r=0.9 and r=0. These are extreme correlations but it is nice to see they are similar to the SMD calculated using the p value (i.e., that in Test1.jpg). If you want a reference for the CMA software, use the book by Michael Borenstein et al. (i.e., Introduction to Meta-Analysis, Publisher: Wiley [2nd edition], 2021). SD of Difference = SE x square root of 239
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Hello all.
Essentially I am carrying out a process evaluation of an online intervention that is delivered to CYP with tic disorders. The main outcome measure is the Total Tic Severity Score (TTSS) as measured on the Yale Global Tic Severity Scale which is rated from 0-50 (higher scores indicating higher severity). The participants were measured on this scale at baseline and then 3-months later (primary end-point) to see if the intervention worked or not.
As part of the process evaluation, I need to see if there were any mediators or moderators on the intervention group only (PE's do not look at the control group). So I would like to know what would be the best statistical test to carry out this analysis, please?
So to break it down:
Dependent variables (both continuous):
TTSS at baseline
TTSS at primary end-point (just looking at one group only i.e. the intervention group)
Moderators:
Age (dichotomous)
Gender (dichotomous)
Index of deprivation (continuous)
Method of referral (dichotomous)
Medication use (dichotomous)
Comorbidities (dichotomous)
Parental education level (categorical)
Mediators:
Treatment acceptance and satisfaction (continuous)
Level of engagement with the intervention (continuous)
Change in mental health (continuous)
Any help would be much appreciated! Many thanks.
Kareem
PS. I only know how to use SPSS!
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You can perform simultaneous nonlinear relationship analysis as in psychometric network models that are based on partial relationships and report centrality indices as mediators and include those with modulation or moderation characteristics in the analysis.
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When we perform housekeeping genes (such as GADPH) for baseline comparison control in RT-PCR analysis, the degrees of CT values were found to vary a lot between different animals (in our case, 5 control rats) from 17-22. But we have to choose one CT value for baseline comparison of other genes of interest. Do anyone encounter the same condition and what is the solution?
Thanks a lot in advance!
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Thank you!
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I have enrolled more patients (362 against the samples size 153) due to loss to follow-up. now the baseline data of all patients show some interesting picture and I want to publish it. the question is, Do I have to mention in methods about the original design, the back ground of these samples? Thank you
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Andrew Paul McKenzie Pegman Thank you Doctor, adding something that can bring a smile is beauty :) Thank you :D
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Hi sir/madam,
Hope you are Healthy and doing well,
In my study I have 2 experimental groups (patients with neck pain and sleep disturbances at baseline) these two groups are equal at baseline in almost all variables and one control group (healthy participants).
After 6 weeks of interventions I have taken the post readings.
Is 2 way repeated measure ANOVA suitable test to use ? Or other test could be more accurate?
If 2 way repeated measure is suitable, should I incorporate covariate in the analysis or keep it blank ?
Thank you
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Hi Mosab,
I hope you are doing well.
In this case, where you have got 3 groups and 2 time points of measurement, you should go for 3X2 repeated measured ANOVA. ANOVA will be applicable subject to type of data (only if the data is continous, you can go ahead with ANOVA) and normality of the data (log transform the if non-normal). These are basic assumptions to any parametric test.
Regarding including the covariates, you need not to add any co-variate if you have not proposed any co-variate adjusted analysis. If in case, any variable comes out to be significantly different between the groups at baseline, then, you can go for analysis of covariance (ANCOVA) where in you can put the baseline value as a covariate.
Thanks & regards,
Dr. Pooja
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I have a problem calculating the final value of a study.
Knowing the baseline of a parameter in mean & SD form and the change rate after the treatment in mean & SD% form. How could I calculate the final effect as in mean & SD form.
eg. a biomarker
baseline: 0.7+-0.1 mg/kg
change: 6+-5% after a year of treatment
final effect: ?
Thanks!
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See the attached screenshot for details. David Booth
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I am looking for a calculation procedure / advice how to figure out the duration of a baseline recording - or rest state recording for an EEG experiment.
Let's say I'm interested in Theta activity occurrence during a 60min task which will be recorded.
Is there an optimal rest state recording duration?
Most of the literature used 5-minutes baseline recording, is there evidence for these 5 min?
Thank you for any help on this,
Melanie
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I'm trying to determine sample sizes for some straight-forward studies looking at quality of life measurements pre/post intervention using multiple survey tools. I'm using effect size from previous studies in my sample size estimation via power analysis, but most don't provide the standard deviation of pair-wised changes needed to calculate a Cohen D for dependent samples. What most of these do have, however, is the mean change from baseline with a 95% confidence interval.
I can see in the Cochrane Handbook 6.5.2.3 (https://training.cochrane.org/handbook/current/chapter-06#section-6-5-2) a method of obtaining the SD for differences in means using CI to calculate SE, then SE and group n's to calculate SD. My question is whether this method is only valid for getting the SD for the difference between two independent groups (as the example in the handbook shows) or could it also be valid for getting the SD of difference between baseline and post-intervention within the SAME group?
P.S. I know the topic of imputing SD with dependent samples when you only have summary statistics is a previously discussed topic here. After hours of reading I'm still having trouble determining clearly if this is even possible.
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We have two columns we use for our HPLC, both Restek, an amino column, and a C18 column. I just ran about 90+ samples on the C18 column and there are no baseline issues at all. So that rules out mechanical issues with the HPLC itself (pump, UV detector, etc)
Now with the amino column, we have been trying to get separation on sugars. A past lab mate got great separation with this column back in 2019, ever since then we haven't been able to get the same type of separation with the same instrument parameters she used. We are currently using 75% Acetonitrile and 25% water as mobile phase and 2ml/min flow rate, which is what was used previously as well. The baseline isn't stable at all and the UV detector just keeps saying "OVER".
We have backflushed the column with different solvents per Restek recommendation and we still have the same problem.
We also did an 8 hour 0.2ml/min IPA flush with no column. Still, no separation and a crazy negative baseline. There are absolutely no pressure problems either.
Does anyone have any suggestions?
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Hi everyone.
I am going to perform behavioral tests, rotarod and Morris water maze, in rats before and after drug administration (7 and 14 days after the first application). In each test, training sessions and baseline data are included. I am not sure if I have to include training sessions at each time point or if the previous training session performed before treatment is sufficient.
Thanks in advance.
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Yes
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question edited as no longer applicable
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Your comments imply that your method may be invalid. Could you provide us with the peak retention time(s) using your above method? A chromatogram, with scales shown would also be very useful to see too.
Lower flow rates are preferred with most types of MS detectors (some can be run at 1.00 mL/min, depends on the method). However, in all cases, the HPLC column used must be sized properly for the flow rate and method used. Your 4.6mm ID column is the wrong size for your flow rate of 0.25 mL/min and points out the need for training before using an LC-MS system for analysis. A 2.1mm ID column would be scientifically appropriate for flow rates ~ 250 ul/min.
*BTW: I think it is great that you are trying to learn these techniques, but please be aware that it takes many years of professional industrial experience/training just to acquire a basic level of training in HPLC (emphasis on 'basic'). As you are new to this area, please start by learning the basic fundamentals of chromatography before using the HPLC to analyze samples (text books can help to learn these concepts when starting out). Please have someone with HPLC experience and training help you with this project. Data acquired using poor quality methods leads to poor quality conclusions.
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I am doing an analysis of data collected in an intervention study seeking to improve antiretroviral treatment adherence using depression management (using a package of defined strategies ranging from psychoeducation, interpersonal psychotherapy, and pharmacotherapy depending on depression severity). My baseline data showed depression score to be significantly correlated to ART adherence score as well as major depression (when adjusted for other covariates) to be significantly associated with poor adherence. Also, for those who had depression or minimal symptoms (in a per-protocol analysis), the intervention significantly dropped their depression scores compared to controls. Now I want to get the real effect of this change in the depression on their change in adherence to treatment i.e. adjusting for the other covariates in the regression model at baseline. I have two questions; Is this the right approach to this analysis? If yes, how do I proceed in getting the change in adherence adjusted for the other covariates used in the model at baseline?
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If you are interested in reactive depression you may get an idea from our paper:
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Dear all,
I want to see whether a biomarker level at baseline can be used to predict the prognosis after a treatment alone as compared to a clinical parameter?
Which statistical model will be best to investigate it?
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Dear Muhammad,
try to read this is an amazing book of Wout
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We are recording field EPSPs in CA3-CA1 synapses from 2-3-month old WT mice. We can get good EPSPs (size trace in blue) but during the baseline the EPSP gets small (yellow trace) and then large again and cycles over the course of the baseline.
As you can see in the traces, the EPSP changes in size but the fibre volley remains the same, so I don't think the electrodes aren't drifting. We also wrap our recording electrodes with cotton so droplets of aCSF do not form and land on the slice.
Please let me know if you have any suggestions of other things we can check. Thanks!
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How many neurons have you recorded? Did all the recorded neurons exhibit the same pattern?
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Logistic Regression for Data Classification
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Binary logistic regression is a classification procedure. When your response variable is binary (say 0 for negative and 1 for positive), and you choose 0 as a reference category, you can request the predicted probability for each subject. With a predicted probability <0.5, the subject is predicted to be in the “0, negative” class, and a probability of >=0.5 for the other class “1, positive”.
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Hello, I want to calculate the percentage change in rainfall by taking the future rainfall values and the baseline period values. My baseline period is 1961-1990 and future values are 2041-2070 and 2071-2099. I want to know this baseline period values should be taken from the observed rainfall data or model simulated one for the same period?
Thank you in advance.
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The base line data should be observed or measured rainfall. Observed rainfall is normally used for calibration. So you can find the mean for the base year and compare this mean with what you get in future.
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The study aim investigated the relationship between meal frequency and timing with changes in BMI. Based on the cohort study data of meal frequency obtain during last follow up and changes in bmi comparing bmi at baseline and last follow up.
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retrospective cohort study,as you went back to study relationship between exposure and outcome
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Hi guys,
We have been doing single molecule TIRF experiments with virus protein Gag to look into its assembly process. When analyzing the data, some traces look nice, but some have this baseline drifting problem. We tried some optimizations but the problem is still there. We have problems in:
1) as shown in Fig 1 attached, we think there is a clear baseline drifting (signal gradually decreasing around 0 intensity), and
2) in some of the slides, the overall signal is very intense for the first couple of seconds (Fig 2 attached), and then the image gradually become more "normal" (Fig 3 attached). This has been making our data processing process very difficult, and we are not sure if this is also related to the baseline drifting problem.
The buffer we are using contains 100 uM propyl gallate, 2 mM DTT and 4.5 mM trolox. We are kind of new in single molecule TIRF, and would really value expert opinions in how to improve/optimize the system.
Thanks in advance for any input! We really appreciate your help!
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If I understand, you are saying there is general background signal that is slowly bleaching? I've encountered fluorescence in medium that bleaches with illumination (and then recovers due to diffusion). Riboflavin is one culprit that is in culture medium and is fluorescent. I was told it can even stick to glass.
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I would be grateful if anyone could provide me with a real data set consisting of residential customers’ smart meter readings to use for customer baseline load (CBL) estimation.
Regards,
Zohreh
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Salam,
Check this website http://wzy.ece.iastate.edu/Testsystem.html , you may find the data you are looking for.
Regards,
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Good morning,
We have different sets of data for MHD patients on different time points and the problem is- we are missing patient in each time point (death/transfer to another hospital/underwent transplant/drop out due to unknown reason) and now the number of patients survived is almost half compared to the baseline patients. It will be really helpful if anyone could suggest a suitable and advanced statistical procedure by which we could see the changes in patients' health outcome over time.
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For the sake of clarity, could it be said that the treatment administered for theresearch purpose is responsible for the death, transfer, drop out and other? These are actually your parameters for measurement of difference.
Values must first be taken for each and every time point based on these parameters
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I have the following design; Independent variable categorized as (Present and Absent) the dependent variable is BMI measured at (baseline line, week 24, week 48, week 96 and week 144)
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A 33 week woman presented with bleeding and non hypoxic trace, a subsequent trace showed a deceleration of 4 minutes but recovered to a baseline of 140bpm. Consistent with previous monitoring and antenatal auscultation.
Following Magnesium sulphate as woman thought to be labouring baseline 110bpm all other features normal, cycling present and accelerations. Why??
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The baseline of PVA jumps suddenly towards higher intensity (on the Y axis) on reaching 200 degree Celsius.
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Sergey Mamedov Souvik Bhattacharjee - if the sample does not absorb, it does not fluoresce. Mukesh Pandey - you did not specify what wavelength you are using, but I presume it is in the visible, where PVA does not absorb.
The large broadband background in Raman is very often due to scattering which enters the spectrometer as stray light, and therefore some morphological changes in PVA at elevated temperatures which increase the scattering could more likely explain the observed changes in Raman background.
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Hello, recently I faced some issues with DataAnalysis 4.2 sofware of Bruker. In example a take chromatogram from analysis for processing, perform smoothening of chromatogram and subtracting baseline of smoothened chromatogram. So in sample field we have tree with original chromatogram (level 1), smoothened chromatogram (level 2) and substracted baseline chromatogram (level 3). After saving of this analysis and reopening, data of level 3 is not shown in chromatogram field, but if I delete level 2 and substracted baseline chromatogram goes to level 2 data appears. Any suggestions how to solve this problem that after reopening saved analysis all levels will be showed? Thanks
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Hallo, I would send an email to the Bruker group. They are always helpful :)
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Hello,
There's an article on Ashwagandha at:
It's a crossover study, switching between ashwaganda and placebo. It seems that for the first period (8 weeks), testosterone levels decreased for ashwaganda group from baseline 354.22pmol/L to 332.77pmol/L. It's not explicitly mentioned in the study, but I would appreciate if anyone could verify if that is true? I'm not sure I'm reading the cross-over study correctly. It also seems unusual given the research indicating it increases testosterone.
Thank you,
Joe.
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Good question
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I have downloaded SDSS specra data of the dwarf galaxies for my study. I am studying strongest emission lines. I am bit confused, whether we need to perform a baseline correction beofore the measurement or not. Need your help. Thanks.
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Yes, in order to measure the line flux (and subsequently - luminosity) you need to subtract the continuum. There is a simple explanation for this. Imagine that your galaxy has 10 times more stars but the same SFR. Then the continuum level will go up by a factor of 10, so if you don't subtract it you would measure SFR 10 times higher, which would be wrong.
By the way, check the SDSS catalogues - maybe your galaxy already has the H alpha flux measurement.
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Good afternoon, I am writing because I am recording with Fura-2AM to observe the response to intracellular calcium to different compounds. I observe the response at two different times, at 60 seconds and then at 400 seconds. It happens that in the course of time between the first and second answers, the baseline tends to rise and I have not been able to know what this problem is due to. Somebody could help me?
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Refaire expériences
Ou changer appareil
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I asked a question on disaggregating daily rainfall data to hourly data here:
Now, my question is if the following way for disaggregating rainfall data works well in your idea?
We have both the daily rainfall time series (i.e. output of GCM models) for the future time and the observed hourly rainfall data for the baseline period. We consider the temporal distribution of future rainfall for each day according to temporal distribution pattern of observed data (assuming the rainfall temporal pattern in future and baseline is the same).
I'd like to know if this method seems reasonable.
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Hi! I'm trying a software named NETStorm. It has disaggregation module for daily rainfall. You can disaggregate the daily data based on the hourly pattern of a station located in a common area. I'm still trying to completely understand the theory behind it, but s far it has been the best option I have found for this matter.
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If I want to take the uv spectra of an enzymatic reaction which is the best way to do it? What troubles me specifically is the blank. What is the best way to set my baseline? Using buffer + substrate? Using buffer + substrate + inactivated (boiled) enzyme (crude cell extract with the overexpressed enzyme)? Thank you all in advance for your help.
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You can use buffer + substrate as blank if your enzyme's absorption range is different than your product's range otherwise the absorption of inactivated enzyme can interfere in your results
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So in my systematic review, I have included mostly studies that include baseline, end and change from the baseline data for the outcome, however, one study present only the change from the baseline for two treatment groups. I don't know how I should be able to make a fostest plot from this data. I'm using RevMan5.
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Try to contact the corresponding author and I guess RevMan Calculator would help you.
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I am doing a thesis. The aim of the study is to see the effect of STI infection during pregnancy on adverse birth outcome. All volunteer pregnant women were screened for STI at certain week. Then few of them were positive, others did not infected at that moment and other don`t know their status. I consider this screening as baseline. Then exposure status for STI infection was assessed every two weeks until they give birth, as a result the infection status for each individual vary. The gestational age at baseline vary from individual to individual therefore their delivery date is also vary. which means the outcome measurement date is their delivery date. My questions are:
  1. Can I say this study is a prospective follow-up or cohort study design?
  2. Which date is considered as a baseline; the first STI screening date or the STI infection date (if this is the case, each individual can have different baseline date)
  3. If it is follow-up / cohort study, each women have different follow-up number because of different gestational age when the survey began and their expected delivery date vary. Therefore, how can I handle the administrative missing data?
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Thank you .
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This is a PL spectrum of my sample. It seems like I need to set a baseline for this. But I am confused as no one generally sets the baseline for PL.
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Try to change Excitation and emission slits into 15 and 5, respectively. Also dulute the sample to get better readings. You can follow the attached article.
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For my bachelor thesis I am planning to do baseline measurement, an intervention and a follow up test on the treatment group.
How would I deal with dropouts? Do I have to include them in the statistical analyses? For instance if a participant does the baseline test but doesn't participate in the intervention?
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There is an approach used in psychopharmacological research known as "last observation carried forward". This simply aggregates the data, without accounting for length of time in treatment.
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Can anyone explain me how can i calculate the ird in single case research when the most of the data points in the baseline are over than the data points in the treatment phase?
I have read the REICHOW's article with the method but in some cases i don't understand it..I don't understand how exactly we write a straight line across the data points to find the overlapped datas
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I am more familiar with non-overlap measures of effect size, but hope these help!
Kim, D., An, Y., Shin, H. G., Lee, J., & Park, S. (2020). A meta-analysis of single-subject reading intervention studies for struggling readers: using Improvement Rate Difference (IRD). Heliyon, 6(11), e05024.
Parker, R. I., Vannest, K. J., & Brown, L. (2009). The improvement rate difference for single-case research. Exceptional Children, 75(2), 135-150.
Parker, R. I., Vannest, K. J., & Davis, J. L. (2011). Effect size in single-case research: A review of nine nonoverlap techniques. Behavior Modification, 35(4), 303-322.
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Hi,
I am using a linear mixed model to analyze longitudinal data. In my restructured database, Time is named Index1. SPSS automatically uses the final timepoints as reference. I want to know the changes in regard to baseline, so I would really like to see numbers for Index1=2, Index1=3 and Index1=4 in stead of Index1=1, Index1=2 and Index1=3.
I think I can do this mathematically, but it would be so much nicer if I could do this in SPSS...anyone an idea?
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You can always use the compute function to transform the index values to a new variable, maybe "Index_Rev" which is combuted as Index x -1 which would yield -4 to -1.
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In this article on Safed Musli: , the authors write "Statistically significant improvement was observed in serum testoster