Science topic

Recurrence - Science topic

The return of a sign, symptom, or disease after a remission.
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As I know, there is only one program that can calculate cumulative incidence: XLSTAT. Is there any way to calculate in SPSS with %95 confidence intervals, to compare two treatments?
I have two treatment groups. I recorded the day of relapse and day of "death or other competing events".
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Hello Vedat,
I believe that you'll find many statistical packages are capable of quantifying cumulative incidence (as well as CIs for any given time point) other than XL:STAT.
Have a look at these links for more detailed explanation:
Good luck with your work.
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Hi,,
I'm planning to run cut&run for H3K27me3,EZH2 and JARID2 profiling in matched paired (primary and recurrent) glioblastoma samples. I'm looking for a positive control to validate the antibodies for EZH2 and JARID2 for cut&run using qPCR, any recommendation or suggestions based on your experience?
Thanks,
Muna
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H3K27me3 histone modification is actually a good positive control.
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I have a problem with finding references for high-order generating functions. For example in finding explicit formula of this recurrence relation: https://mathoverflow.net/questions/266478/linear-two-dimensional-recurrence-relation
Actually, in my research, there is a three-dimensional recurrence relation. Does anybody have some books about high order generating function in general?
I really appreciate any help you can provide.
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Peter Breuer Thank you for your kindly reply, Sir. Yes, that's the generating function that I mean. Do you have any references (papers or books) about this thing? Especially for a higher dimensional.
I appreciate any help you can provide.
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  • To estimate suitable petrophysical properties, would it will be useful to use CNN or GRU algorithms alone?
  • Or, will this estimate will be appropriate when the two methods are combined?
  • Convolutional_Neural_Network: In deep learning, a CNN is a class of artificial neural networks, most commonly applied to analyze visual imagery. They are also known as shift invariant or space invariant artificial neural networks (SIANN), based on the shared-weight architecture of the convolution kernels or filters that slide along input features and provide translation equivariant responses known as feature maps. Counter-intuitively, most CNN's are only equivariant, as opposed to invariant, to interpretation.
  • Gated_Recurrent_Units: GRU is a gating mechanism in recurrent neural networks, introduced in 2014 by Kyunghyun Cho et al. The GRU is like a long short-term memory with a forget gate, but has fewer parameters than LSTM, as it lacks an output gate. GRUs have been shown to exhibit better performance on certain smaller and less frequent datasets.
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Dear Erfan Rahimi . See the following useful RG link:
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Dear collegues.
I would like to ask,if anybody works with neural networks,to check my loop for the test sample.
I've 4 sequences (with a goal to predict prov,monthly data,22 data in each sequence) and I would like to construct the forecast for each next month with using training sample size 5 months.
It means, I need to shift each time by one month with 5 elements:
train<-1:5, train<-2:6, train<-3:7...,train<-17:21. So I need to get 17 columns as a output result.
The loop is:
shift <- 4
number_forecasts <- 1
d <- nrow(maxmindf)
k <- number_forecasts
for (i in 1:(d - shift + 1))
{
The code:
require(quantmod)
require(nnet)
require(caret)
prov=c(25,22,47,70,59,49,29,40,49,2,6,50,84,33,25,67,89,3,4,7,8,2)
temp=c(22,23,23,23,25,29,20,27,22,23,23,23,25,29,20,27,20,30,35,50,52,20)
soil=c(676,589,536,499,429,368,370,387,400,423,676,589,536,499,429,368,370,387,400,423,600,605)
rain=c(7,8,2,8,6,5,4,9,7,8,2,8,6,5,4,9,5,6,9,2,3,4)
df=data.frame(prov,temp,soil,rain)
mydata<-df
attach(mydata)
mi<-mydata
scaleddata<-scale(mi$prov)
normalize <- function(x) {
return ((x - min(x)) / (max(x) - min(x)))
}
maxmindf <- as.data.frame(lapply(mydata, normalize))
go<-maxmindf
forecasts <- NULL
forecasts$prov <- 1:22
forecasts$predictions <- NA
forecasts <- data.frame(forecasts)
# Training and Test Data
trainset <- maxmindf()
testset <- maxmindf()
#Neural Network
library(neuralnet)
nn <- neuralnet(prov~temp+soil+rain, data=trainset, hidden=c(3,2), linear.output=FALSE, threshold=0.01)
nn$result.matrix
plot(nn)
#Test the resulting output
#Test the resulting output
temp_test <- subset(testset, select = c("temp","soil", "rain"))
head(temp_test)
nn.results <- compute(nn, temp_test)
results <- data.frame(actual = testset$prov, prediction = nn.results$net.result)
}
minval<-min(x)
maxval<-max(x)
minvec <- sapply(mydata,min)
maxvec <- sapply(mydata,max)
denormalize <- function(x,minval,maxval) {
x*(maxval-minval) + minval
}
as.data.frame(Map(denormalize,results,minvec,maxvec))
Could you tell me please,what can i add in trainset and testset (with using loop) and how to display all predictions using a loop so that the results are displayed with a shift by one with a test sample of 5?
I am very grateful for your answers
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Dear colleagues, I've tried to construct some recurrent neural network with using learning sample size 25 and I would like to get 178 columns in output as a result (there are 25 columns and 178 linesin the learning sample),but I can use predict ony for a single item :
pred<-predict(fit,inputs[-train[106,]]),so I need to change the numbers in train to get a column with forecast.
sslog<-as.ts(read.csv("k.csv"))
mi<-sslog
shift <- 25
S <- c()
for (i in 1:(length(mi)-shift+1))
{
s <- mi[i:(i+shift-1)]
S <- rbind(S,s)
}
train<-S
y<-as.data.frame(S, row.names=FALSE)
x1<-Lag(y,k=1)
x2<-Lag(y,k=2)
x3<-Lag(y,k=3)
x4<-Lag(y,k=4)
x5<-Lag(y,k=5)
x6<-Lag(y,k=6)
x7<-Lag(y,k=7)
x8<-Lag(y,k=8)
x9<-Lag(y,k=9)
x10<-Lag(y,k=10)
x11<-Lag(y,k=11)
x12<-Lag(y,k=12)
slog<-cbind(y,x1,x2,x3,x4,x5,x6,x7,x8,x9,x10,x11,x12)
slog<-slog[-(1:12),]
inputs<-slog[,2:13]
outputs<-slog[,1]
fit<-elman(inputs[train[106,]],
outputs[train[106,]],
size=c(3,2),
learnFuncParams=c(0.2),
maxit=40)
#plotIterativeError(fit)
y<-as.vector(outputs[-train[106,]])
#plot(y,type="l")
pred<-predict(fit,inputs[-train[106,]])
a<-pred
print (a)
df <- data.frame(a)
Could you tell me please,how is possible to construct data frame and to get the 178 in output as a result,not only single column?
Thanks a lot for your help
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Try looking on the attached screenshot for a.trick. but you don't have much to predict from..so ??????. David Booth
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Dear collegues,
Could you tell me please, if anybody may to provide,please, a working code for the recurrent neural network for the real data (with using training set) in R?
For example, keras and rnn.
Thanks a lot for your help
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Hello everybody
According to what mentioned in the attached snapshot from the appendix of this article
My question is, if we already have the transition probability matrix P, how could we calculate numerically v^=α0(I−P)−1, as the limit of the recurrence v(t+1)=v(t)P+α0?
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I have got the full text of your book. It looks great.
Thank you very much for your fruitful comments.
Regards,
Ahmed
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Is it really necessary to open the external oblique aponeurosis in performing open inguinal herniotomy ? or performing inguinal herniotomy via the external ring is equally effective ? and does that ( opening the inguinal canal ) really increase that risk of Ilioinguinal nerve injury ? is there any increased risk of recurrence if done via the external oblique ? feedback regarding institution experience is highly appreciated
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In the pediatric center where I work, our standard is to correct inguinal hernia via external inguinal ring in all patients.
Hernias in the pediatric patient are indirect, cause by persistency of the peritoneal-vaginal duct. As children are quite "plastic", we belive that this approach asures a correct high ligation of the duct. In my 6 years of work, there was only one 14 year girl that had previous inguinal hernia repair, which experience recurrent inguinal pain and upon second surgery, we found an incomplete ligation of the peritoneal-vaginal duct.
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Hi, could I get some suggestions about how to determine (1) the depth of water that infiltrates into the soil profile; (2) whether surface runoff will occur, and if so, how much.
Assume that for each scenario (a-b), the antecedent moisture content is at field capacity.
· Root zone depth = 1 m
· Ksat= 15 mm/hr
· % wilting point = 0.15
· % field capacity = 0.25
· % sat = 0.35
a) A rainfall event with an average 1-year recurrence interval and a duration of 60 minutes of 1.11 in
b) A rainfall event with an average 1-year recurrence interval and a duration of 24 hours of 2.51 in
Thanks.
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The wave of pandemic Corona is still on.
Many get infected, Some died and a lot more got cured.
Now the question is -- what is the chance of recurrence after recovery? Is there any possibility of re-inception ??
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14 percent of patients who recover from COVID-19 have had it again. I thought they weren't infected again, but it's a "reflux" case of the virus.
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How can I treat patient with post treated HCV mixed cryoglobulinemia relapse after sof/dakla after SVR, 6 months... Presented with peripheral neuropathy and can't stand or walk with PCR HCV RNA negative with positive cryoglob. With high tire?
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cryoglobulinemia is a rare disorder characterized by the presence of cryoglobulins in the blood. Cryoglobulins are abnormal proteins that thicken and clump together at cold temperatures, usually below 98.6 degrees Fahrenheit (the average human body temperature)
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there are many side effects of chemo and radio therapy for cancer therapy, moreover, one of the main obstacles is cancer recurrence !!! I would like to know, does genomic engineering approach for cancer therapy gives permanent results or there is still a risk of cancer recurrence
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Frank Mitter thanks for your comment, yes it was very internesting
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Please justify your opinion:
whether fusion is necessary for recurrent lumbar disc prolapse.
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Dr Md Moshiur Rahman That is an excellent & debatable question. These are the principles I try to follow in making this decision:
  • rule out possibility of perineural fibrosis or arachnoiditis causing recurrent symptoms, instead of a true recurrent disc herniation.
  • Is back pain worse than leg pain? Axial back pain tends to be mechanical rather than neural, and likely benefits from fusion.
  • Presence of radiographic instability (spondylolisthesis on standing films but not on supine images, changes on dynamic flexion-extension films, large facet diastasis on MRI). These indicate need for fusion.
  • Extent of facetectomy/bony resection in initial surgery may favor fusion
  • severity of disc degeneration (Modic changes, vacuum phenomenon) may favor fusion.
Other than obvious instability, all others are relative indications for fusion. One has to realize that fusion is a bigger surgery (more blood loss, operative time, length of stay, postop rehab), and has risks of adjacent segment degeneration, instrumentation-related complications and reoperations. It is a non-physiological remedy, and certain occupations may have restrictions for returning to work after fusion surgery. Lastly, recurrent discectomy is a reasonable choice for the first recurrence (with absence of instability). But second recurrences should likely be fused. Hope this helps. Thanks
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We have seen the quarantine is the best remedy for covid. However we can't stay at home all the time. Labour hours, working at home is not adapted now. Can we find a way to approach a solution, better without a quarantine, to find an equilibra between our lives and our health? This summer has been hantavirus that might have faded away, and it could be the first time the bubonic plaque has reached China from Mongolia, where there has been cases in USA, California(and before covid) How does covid really affect the new diseases and how will it affect to us now?
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Online classes are going on now also.
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Throughout my research related to mixed-race identity development in Japanese context, most participants report differences in how their self-ascription differs from that of their parents. I would like to know if this is a recurrent phenomena in other contexts as well.
Thank you very much for your reply,
Kind Regards,
Alexandra Shaitan.
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Dear Dr. Shaitan Alexandra,
I have been greatly shocked by your kind, thoughtful and profound observations about my contribution to your Question and I hope to continue contributing material for your University Research work.
With my respects.
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I have a model of depression which I am attempting to convert into a functional simulation in the form of a recurrent neural network or genetic algorithm, whichever would suit it better. (The model is attached)
Some of my questions on how this may work include:
  1. How ought one optimise the initial parameters of the system?
  2. How best to deal with positive feedback-loop induced instability?
  3. How to go about choosing between activation functions
Any suggestions on how to go about coding an initial loop and then expanding it would be very helpful. Also, any suggestions on where to code this, I was thinking Jupyter.
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I would like to identify the predictors of seizure recurrence after a first seizure. Should I use a Cox regression model yielding hazard ratios or should I use a log-binomial regression yielding risk ratios?
From what I understand, I shouldn't use logistic regression because it yields odds ratios, which often overestimate risk ratios.
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This comes down to whether the time-to-event component is clinically relevant or not. If it is, then you should do Cox regression, and otherwise logistic or as you suggest log-binomial.
For rare events, odds and risk ratios will be almost identical. For more common events, odds ratios will be higher but it isn't a case that they are 'overestimating' the likelihood, they are just a different effect measure. There is nothing wrong with using logistic regression in this instance either.
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Stress is a fact among the researchers' community, wherever you are and whatever you are doing. It can be cyclical, randomly recurrent, ephemeral, or chronic.
Is there any practical guide [good laboratory practices] to prevent burnout?
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The Stress that is generated in the Laboratories is similar to the Stress of many other jobs; in fact, except for the Hospital Clinical Laboratories (of Microbiology, Hematology, Pathology, etc.) that YES that their members are subjected to clinical pressure already on Stress, and more with the pandemic, they do not appear in the "ad hoc" literature. as sites that suffer, with few exceptions, a special stress.
For the framework of such Hospital Clinical Laboratories it is recommended to use the standard Coping and Stress Management Techniques (both at the worker level and at the organizational level), in this sense I recommend reading, here in "RG", our work: "Intervention and Prevention of Occupational Stress " (Intervención y Prevención del Estrés Laboral) and, for a specific approach to Healthcare Personnel, the book by JJ Gestal Otero" Risks of Healthcare Personnel Work " (Riesgos del Trabajo del Personal Sanitario), in the Inter-American Publishing House-McGraw Hill. Thanks
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I have some binary values, lets say 0.5 and -0.5 (remember not 0 and 1), I want to use RNN to predict these values. Is it possible using RNN?
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What is the role of Retroperitoneal lymphnode dissection for a synchronous metastatic RCC, status post Cytoreductive nephrectomy with unaddressed enlarged retroperitoneal nodes during surgery?
Is there a evidence to suggest staged RPLND after a gap of 6 months or shud Oral TKI is preferred?
Patient has a recurrent scalp lesion involving the occipital region with an intracranial extension, however resectable
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We know from the literature (Andrich and Mundy, Barbagli and mamy others authors) that recurrence rate after buccal mucosa graft urethroplasty (BMGU) done for urethral stricture increase every 5 years from the operation, reaching 30-40% or even more 15 years after the operation. It seems reasonable to try to prevent those complications actively instead of using "watch and see" passive method of postoperative observation and perform next BMGU if recurrence of urethral stricture appear. Don`t you think that our proposition of calibration of urethra with antiscar gel used as a preventive method for stricture recurrence may be some solution of that problem? We don`t waste time during scar remodelling which starts early in postoperative time, followed by scar contraction, the main cause of urethral stricture recurrency.
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Patients are very motivated to continue self-calibration with anti-scar gel because they suffereda lot for a long period of time due to serious problems with voiding as well as due to the painful memory of many operations followed by recurrence od urethral stricture.
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In a DCLD patient who is on diuretics, the recommendation in a patient with ascites and AKI is to stop diuretics and start albumin. But when the diuretics should be restarted remains a dilemma, because these patients are commonly prone to recurrent AKI.
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When normal s.cratinine
Normal creatinine clearance
When developing any urgent need for diuretic
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Hello, I am a master's student studying in Yonsei Univeristy, Korea.
I am trying to estimate the state of satellite, using Neural Network.
Below is a simple flow of my study.
1. Train (t0 ~ t1)
Train neural network using known observation & true state data
2. Validation (t1 ~ t2)
Using observation data starting from t1, validate the network
3. Test (t3 ~ t4)
With new observation data, estimate the true ECI coordinate at different time.
[For all steps]
Input : observation data ( RADAR SEZ coordinate data or Orbital Element data )
Output : true data ( ECI coordinate data)
I know that the validation is already done while training,
but the validation part is for checking whether the network is well-trained.
I used "narxnet" from the deep learning toolbox, and it worked well until the validation part.
However, in order to use the network made with "narxnet" for the test part,
I had to retrain using data from just before.
(to estimate t3~t4, need tx ~ t3 data trained network)
So all my work have failed, and I am going to restart on doing this.
Here is what I want to ask.
  1. I found that most of codes in MATLAB related to neural network is for image training. Is it better to use other program for this type of work? (e.g. Python, Tensorflow...)
  2. I found that is it better to use recurrent neural network, and time series input. Is MATLAB "train" code available for this?
  3. I cannot find much information on the documentations. I would like to know if there is good example I can refer to.
Thank you very much for reading my questions.
Jee Hoon, Kim.
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Hi Kim,
I am not an expert in your applicaition domain.
This being said, I can answer your questions in a general context as:
I found that most of codes in MATLAB related to neural network is for image training. Is it better to use other program for this type of work? (e.g. Python, Tensorflow...)
>> I suggest you do not use MATLAB.
In my experience best combination is: Ubuntu + TensorFlow + Python
I found that is it better to use recurrent neural network, and time series input. Is MATLAB "train" code available for this?
>> Never looked into this, so I can't give you a definitive answer. However, recurrent NNs are not that different than regular ones. The underlying (temporally pipelined) architectures are the same anyway. So, this should be possible but I personally find MATLAB too limiting.
I cannot find much information on the documentations. I would like to know if there is good example I can refer to.
>> Once you switch to TensorFlow + Python combo you will find your chances of finding examples and code snippets to be much improved.
Finally, if you intend to keep working in this direction, you should invest in learning Python. You will find it much rewarding.
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Using available data ,the median time from onset to clinical recovery for mild case approximately 2 weeks and 3 _6 weeks for patients with sever or critical disease. Are patients who recovered from COVID_19 immune from disease or may have viral relapse or re infection?
Clinical, recurrence,relapse,re infection
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The second COVID-19 infection is more severe than the first one.
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Does anyone know publications that addressed the following question?: Do lifetime suicidal behaviors impact on addiction treatment adherence or relapses?
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Those who have suicidal behavior turn impulses into action. This impulsivity definitely affects the treatment and adherence process.
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There have been a number of studies relating vitamin D deficiency to cancer and relapse in different types of cancers, for instance Hodgkin's lymphoma. Yet it does not have any repercussion for medical practice. I believe a large study is needed comparing vit D level in newly diagnosed patients to normal population, and next the progress and relapse of these patients should be monitored. This could turn out to be an important prophylaxes tool.
The role of vit D in Covid-19 patients has recently been studied at last. (Dr Campbell called for such studies in march 2020 based on observed differences in the number of Covid-19 patients with different amount of pigment in their skin while living at the same geographical location) and amazing results have been found. This simple, virtually costless intervention might decrease the number of new cancers especially blood related ones. After all vit D impacts immune system. Current research shows that the adequate value of vit D for proper functioning of the immune system is 40 not 30 ng/ml
It will be interesting to find out, how increased supplementation these day will influence the number of new cancers, though one will need to take into account diagnostic delays of recent months. Would anyone be interested in running such a study? Wouldn't be wonderful if vit D supplementation in new required doses could limit the number of oncology patients if only a little bit? Once vit. D supplementation in children has significantly lowered rickets.
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I am aware of research relating vit D deficience to increased relapse in Hodhkin Lymphoma and many sililar published studies. So I do not infere my hypothesis from the role of vit D in infections desease. This is a research topic pro publico bono up for graps. I mentioned Covid-19 related studies to show that no one was interested in them for quite a while. Similarilyy no-ne is interested in implementing the findings of the research I am aware of. But you gave me a good idea to direct the question animal researchers first, before clinical trials on people. Thanks.
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I'm carrying out a retrospective cohort clinical study,
Some patients have visited the hospital twice because of recurrence, with few months in between, but with different symptoms, complications, and cyst size in each time. Even the primary outcome is different in each visit.
What should I do?
Should I consider only the latest visit?
Or treat them like 2 different patients?
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Greetings Sebawe,
I would consider conducting separate reporting for the secondary cyst, this way your main analysis will only include patients primary cysts and if there is sufficient data for patients with recurrence, you can carry out additional analysis (logistic regression or just chi square) to investigate factors contributing to this recurrence. If data on recurrence is limited, then I think narrative reporting for these cases will be enough. Also, adding a yes/no variable in your main analysis for recurrence is a must, regardless if you include recurrence as a separate patient (which is not recommended) or have them in separate analysis.
Nevertheless, clinical insight in this scenario is a must.
Hope this helps.
Good luck!
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I would like to know whether it is possible to model competing events while including the type of observation period as a covariate? For example, if I wanted to model competing events in two different tasks, one that lasted 3 mins and the other 5 mins. These are two different observation periods, but can events from both be included in the same analysis?
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Second-by-second observation should be more than adequate to characterize hazard rate functions. You will still need to have a sufficient number of events of each type to characterize hazard rate functions with any precision, particularly as you add parameters to convey time dependence. Hopefully this is helpful to you.
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Urinary Tract Infection is more likely to occur in young women especially those who are sexually active or pregnant, which puts them at a higher risk for the infection. It can be a single-episode of Urinary Tract Infection or a recurrent UTI. The incidences of Enterococcus faecalis and Escherichia coli shows to be significantly higher in patients with infection than those who had single-episode urinary tract infection. E. faecalis is known to be the most common and make structural changes. Adherent E. coli is also more likely to have an important role in the etiology of young women who have recurrent UTI. Both of these bacteria are known to cause mild to serious diseases. So the question is, what clinical signs and symptoms will distinguish recurrent UTI from a single-episode UTI?
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I agree with the excellent and comprehensive answers from Mary CR Wilson.
Just to add recurrent UTI's occurring in the context of an incomplete course of antibiotics, and /or resistance to the prescribed antibiotic, anatomical bladder abnormalities (diverticulae, calculus), functional -vesico-urethral reflux, renal calyx- pyelonephrosis, calculi, underlying co-morbidities (diabetes) and perimenopausal changes in estrogen levels.
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In terms of quality of life versus quantity of life?
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Makhdoom Sarwar, as with any medical treatment, it comes down to discussion with the patient and what is important to them.
Some people want a long life at any cost, others want to avoid possible side effects. Again, what gives life quality is different for everyone, some people want to be free of pain, others want to be able to be around family, others get quality of life from being able to work in the garden or play sports.
There are different reasons to prescribe the same drug, this is reflected in choice of dosage and period of treatment.
Does this answer your question?
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Dear All,
can anyone assist in assessment of VEGF-D concentration in a 15-yr old girl with recurrent pneumothorax (Poznań, Poland)? We need to rule out LAM as a differential diagnosis.
Best regards,
M. Mikoś
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If you haven't done HRCT, that would be the first step.
VEGF-D blood tests are assessed at Cincinnati Children’s Hospital Medical Center.
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TLR4 and recurrent UTIs, is there any correlations?
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Recent data showed that TLRs is protective against UTI especially TLR4.
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Can anybody guide me regarding minimum required events (recurrence) for a successful recurrence free survival analysis in oral SCC? I'm expecting 30-35 events in my cohort of 108 patients at 3 years. Will it be good enough?
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The study power depends on the number of events, the total follow-up time in person years, and the ratio between the sizes of the groups being compared.
Simulation studies have tended to the conclusion that you need ten events or more per predictor variable in your model. More recently, bigger and more comprehensive simulation studies have cast doubt on this hard-and-fast rule. Vittinghoff and McCulloch (2007), in a very widely-cited paper, concluded that “problems are fairly frequent with 2–4 events per predictor variable, uncommon with 5–9 events per predictor variable, and still observed with 10–16 events per predictor variable. Cox models appear to be slightly more susceptible than logistic. The worst instances of each problem were not severe with 5–9 events per predictor variable and usually comparable to those with 10–16 events per predictor variable.”
Since then, further simulation studies where prediction models are validated against new datasets tend to confirm that 10 events per variable is a minimum requirement (see Wynants 2015) for logistic regression. These studies are important because they are concerned with the generalisability of findings.
The second factor that will influence sample size is the nature of the study. Where the predictor variables have low prevalence and you intend running a multivariable model with several predictors, then the number of events per variable required for Cox regression is of the order of 20. As you might imagine, increasing the number of predictor variables and decreasing their prevalence both require increases in the number of events per variable.
Based on current research, the sample should have at least 5 events per predictor variable ideally 10. Sample sizes will need to be larger than this if you are performing a multivariate analysis with predictor variables that have low prevalences. In this case, you may require up to 20 events per variable, and should probably read the paper by Ogundimu et al.
  • Courvoisier, D.S. et al., 2011. Performance of logistic regression modeling: beyond the number of events per variable, the role of data structure. Journal of Clinical Epidemiology, 64(9), pp.993–1000.
  • Kocak M, Onar-Thomas A. A Simulation-Based Evaluation of the Asymptotic Power Formulas for Cox Models in Small Sample Cases. The American Statistician. 2012 Aug 1;66(3):173-9.
  • Ogundimu EO, Altman DG, Collins GS. Adequate sample size for developing prediction models is not simply related to events per variable. Journal of Clinical Epidemiology. Elsevier Inc; 2016 Aug 1;76(C):175–82.
  • Peduzzi, P. et al., 1996. A simulation study of the number of events per variable in logistic regression analysis. Journal of Clinical Epidemiology, 49(12), pp.1373–1379.
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I am comparing different machine learning techniques for learning dynamical systems (e.g. a system of ordinary differential equations), and so far I've used Long-Short-Term Memory Networks (LSTM) and other variations of Recurrent Neural Networks, Dynamic Bayesian Networks, and Symbolic Regression.
However, I know only a part of this fascinating domain, so I wanted to ask the community: Can you suggest other state-of-the-art machine learning techniques for learning dynamical systems? Black-box or white-box, it's not important; I am more focused on getting good data fitting for my application.
Thanks in advance for any suggestion :-)
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I have recently explored GANs (Generative adversarial networks) for similar tasks.
give it a try
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I want to do region based classification of speech using dilect analysis. So whether can I use Recurrent neural network for it?
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Hi, in my personal opinion, recurrent neural network (RNN) is an appropriate approach to classify and analyse language dialect, as the input will be the sequence data.
In particular, long short-term memory (LSTM) (an artificial recurrent neural network (RNN) architecture) is recommended to identify, analyse and classify the accent, dialect and speech. You can find some applications of LSTM and RNN on the speech, accent and dialect recognition as shown below:
Hope these information helps!
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Many patients of OCD relapse after cessation of rTMS. Maintenance rTMS may be useful in these patients.
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Following.
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Patients recovered from COVID-19 and discharged from hospital after fulfilling the criteria of discharging (WHO), so any chance of COVID-19 relapse in a recovered patient is there or not?
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There is chance for relapse after recovery from covid-19 therefore preventive measures should be continued to avoid recurrence.
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I'm looking for historical wind speed data in Vietnam for coastal areas. Data can be 10 or 2-min. averaged. The data will be used for extreme wind speed and recurrence period analysis that is why I need data for as long period of time as possible.
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You need to keep track of friends and students who are studying in Australia. They usually work on data from neighboring countries.
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A ten years old female child with recurrent melena. Her upper endoscopy revealed an isolated lesion in the gastric fundus that could be IGV-1 without esophageal varices nor PHG.
What is your experience?
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Thank you Michiel for your answer
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Error using trainNetwork (line 154)
Invalid training data. For a recurrent layer with output mode 'last', responses must be a categorical column vector.
Error in Trails (line 71)
net = trainNetwork(XTrain,YTrain,layers,options);
Caused by:
Error using nnet.internal.cnn.util.NetworkDataValidator/assertOutputModeCorrespondsToDataForClassification (line 382)
Invalid training data. For a recurrent layer with output mode 'last', responses must be a categorical column vector.
I will be thankful to u if ur solution resolves this problem.
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In matlab, and for the deep learning toolbox, there is a class of variables called "categorical".
perhaps they are referring to that.
if your training target data is numerical, or even if your problem is a classification problem, then try to convert the data to class categorical.
it would be something like: categorical(x)
read the documentation
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Curettage and bone cementation is a commonly done procedure in the GCT of bone. The recurrence rates after this surgery are significant. We are trying to find the causes of recurrence and what all could be done to avoid or decrease its incidence.
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A central mandibular giant cell tumor was diagnosed by aspiration puncture biopsy in a 25-year-old female patient. The "in toto" tumor surgery was performed, then the surgical bed was irrigated for 5 minutes with Carnoy's solution (acetic acid, chloroform and ethanol). This Carnoy solution is used to fix and destroy the residual tissue of the lesion by chemical cauterization. The bone defect is filled with iodoformed gauze. The immediate postoperative period showed normal healing due to a second intention and no signs of recurrence were observed in the 15-year clinical-radiological follow-up. In this treatment protocol, bone grafting should not be placed, because chemical cauterization damages the surface of the recipient bone bed.
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I want to optimize the Recurrent Neural Networks weights using the Genetics Algorithm.
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make (Recurrent Neural Network weights) the fitness function
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How does one cure a long-term headache (more than 2 months), which seems to be secondary to recurrent depression in man?
The headache is located in the following regions of the head: left and right orbitofrontal cortices, lower left temporal cortex, lower left parietal cortex and lower left occipital cortex. The pain switches sometimes from the frontal areas to the back areas of the brain.
Could it be something unrelated to depression?
What are the causal underlying mechanisms of such pain?
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Tratment is multiprolonged approach depending on the underlying cause .You may need treatment for both headach and depression.
TAD,MAOI, anxiolytic, botox some time.
To alleviate depression SSRIs( paxil, Prozac)
Some time use paracetamol or ibuprofen
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Our knowledge on Chaos theory is practically zero. Can anyone help us figuring out and explaining pattern of the chaos (please check out the figures only)
We have not found any research paper or textbook referring to this recurrence. Can you help us to explain these problems in terms of chaos theory?
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Recently HPV vaccine is being used in the management of Recurrent Respiratory Papillomatosis. It is expected to deter the recurrence of the disease. I have so far used it in five patients in the last two years. So far the outcome is not very encouraging. I want to know about the experience of the researchers about the efficacy of HPV vaccine in the management of Recurrent Respiratory Papillomatosis.
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Literature shows that HPV vaccination is correlated with an increase in time between procedures and a decrease in number of procedures needed per year-factors that can dramatically reduce the disease burden on patients coping with RRP.
Ref
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Dear everybody!
I do a hobby project as creating a character-level seq2seq2 LSTM.
In my task, I give a text as an input (max 40 characters) and the LSTM generates an output that rhymes with the input.
I created very large rhyming rows databases.
At the beginnings I trained my model with the next parameters:
batch_size = 200 epochs = 250 latent_dim = 300 num_samples = 10000
with these parameters my model converged to 0.4 after 75 epoch, but i waited all the 250 epoch and tested that model.
The result wan't so bad, but I wanted more.
After that I tried very large batch sizes, with more than 200k training data (almost all possible parameres) and every result leads to overfitting, that means my model threw the same sentence to every input. BUT(!) after I tried the 250 epoch model, I used checkpoint saving and tested only the best model after it didn't converge more. It stops at 0.29 acc usually.
I know the character level lstm in this task has its own limitations, but it would be really 10k training data?
Is it possible the convergence doesn't matter in this case and the model needs only more epochs?
Is the database too big and has a lot of stopwords and I need to do word-frequency-based filtering on the training data?
I know that the word-level method could be more effective, but I'm afraid of I misunderstood something and I don't want to waste more time to wait results from training until I don't know what I'm doing wrong.
What should I do?
Thank you all.
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There are two methods which can help with overfitting. One is regularization. Is your output sigmoid? If so, activity regularization on the previous layer is helpful. Otherwise, kernel regularization might be a better bet.
The other common method is to add noise: Dropout to a binary layer, or GaussianDropout to a layer with continuous values. If you are using Keras, there is an option on the LSTM class to specify some dropout. The most commonly used value is 0.5, but I often use less.
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Hello
I am searching for the latest trends in the long term memory neural networks for sequence learning. If you know any model (recurrent or not) good to learn from a large sequence thank you for sharing it with me.
Cordially
Salah Eddine Ghamri
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In principle there are neural networks with internal (LSTM, GRU) or external memory usage (Memory augmented NN). Alex Graves is one of the leading scientists in this field. Also you can use use non-memory models like masked or dilated CNNs or attention based NNs to process sequential data. LSTM and GRU are provided as standard blocks in DL libraries. It merely depends about what relationships you try to model, so only inbetween your time series or over long time intervals. Typically LSTM or GRU are more or less complex filters able to track and memorize changes over maximum 15 or 20 time steps. Attention mechansims can extend this to 40 or 50 time steps. Beyond that i suggestiv to incorporate external memory modules. But this is just my suggestion.
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In the context of time series analysis, there are several multi-step ahead prediction (MSAP) strategies such as the recursive and direct strategies (the two fundamentally distinct and opposed mechanisms). The recursive strategy is the most popular one amongst practitioners. Considering that initial random weights cause inconsistency at the output of RNNs (unless it's been dealt with properly), how to quantify uncertainty over the forecast horizon. I need bands within which the forecasts oscillate.
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Hi Fouad,
Best,
André
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I apologize for asking again the same question, but il really don't have a satisfactory response. In different article, I find more and more (focused on eye movement in reading) that interaction is expressed with "t" value and "b" value. I just ask myself how it is possible. I have try to find a response by without success, even asking directly the concerned authors. I think that my question is not really relevant. But for me, it is.
I known that different solution exist (as anova funtion or chi2) but i would like to know whether it is possible with "t" value.
Thank your in advance for any answer that could remove the doubts?
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Khelifi, if you want say, the (group*freq*predict) interaction( I will call that term int). to get the t information then:
. 1. Make sure that term is in your model , full <- lm(y ~ + x1+....+int) where lm is the R function that you want to call.
2. follow that with summary(full) then
3 t and F information should print out.
here lm is the standard linear model function from base R.which you will replace by the desired function from the package you are using.
Hope this helps, David Booth
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Hi everyone, I am currently working with gene expression analysis. I have downloaded a dataset GSE17537 from GEO database. In this dataset, it shows 19 and 36 patients with cancer recurrence and without recurrence respectively. ( no treatment at all)
how can I find the differentially expressed genes? What basic tool can I use? I find many use Limma but it is too difficult for me!! And how can I calculate the fold change of each gene from this dataset since I don’t know the reference gene ?
thanks for all HELPING hands!!
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If you want only differentially expressed genes from a straightforward analysis, GEO2R can be enough. However, GEO2R may be not flexible for other purposes that R can do, like plotting and data filtering.
For a more complete affymetrix-specific analysis, you'll have to install Affymetrix's Transcriptome Analysis Console (TAC), although data preparation may be difficult for some users. I don't know if they changed something in the recent updates.
Now, if you really want limma in a user-friendly interface, I particularly recommend Gene Expression Analysis Platform (GEAP). It has a intuitive interface where you just put the GSE code and the program gets the entire data for you, then you distribute samples to comparison groups to obtain the differentially expressed genes in a very straightforward way. This program does not need installation (it's portable and can fit in a USB flash drive), and uses R and limma on background to obtain the differentially expressed genes. I did the analysis in less than ten minutes (attached images) using GEAP. It's Windows-only, tough.
Links for each program:
- GEO2R analysis for GSE17537:
- Affymetrix Transcriptome Analysis Console:
- Gene Expression Analysis Platform (GEAP):
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recurrent ectopic pregnancy.
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It depends on many factors if the patient hemodynamicaly unstable then laparotomy might be considered and because it is recurrent adhesions might be seen during laparoscopy this might make endoscopic salpigoplasty imopsible or difficult so every case should be individualized .
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what are the causes of recurrent abortion in women?
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Recurrent abortion is classically defined as the occurrence of three or more consecutive pregnancy loss before 20 weeks' gestation or with a fetal weight of below 500 g; the causes include parental chromosomal abnormalities, untreated hypothyroidism, uncontrolled diabetes mellitus, certain uterine anatomic abnormalities, and antiphospholipid antibody syndrome . Other probable or possible etiologies include additional endocrine disorders, heritable and/or acquired thrombophilia’s, immunologic abnormalities, infections such as toxoplasmosis and urinary tract infection in pregnant women, and environmental factors. Advancing maternal age is associated with an increased risk of miscarriage, which is thought to be due to poor egg quality leading to chromosomal (genetic) abnormalities and possibility of abortion.
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I want to train two deep neural networks on two different data sets. The aim is same in both ( predicting cancer relapse) but data sets contain different type of information. I am looking for the best way to combine these two networks to be able to predict the outcome more accurate.
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Yes you can.
There are three ways I can think of, depending on your requirement.
  1. Have the two neural networks independent and train them separately, but combine the output just like ensemble model.
  2. Have the two networks separate until some points on the networks and make a combination layer somewhere before outfits layer.
  3. Make a brand new neural network using logics and algorithms of the two neural networks.
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Hello,
i would like to performe time to event Data as time varying covariate in competing risk Analysis in Stata?
I have the variables
  • Overall Survival in Months (OSM)
  • Cumulative Incidence of Nonrelapse Mortality (NRM 0=alive, 1=nonrelapse death, 2= competing event Relapse).
  • Time to Infection in Months (TI)
  • Infection (0 no infection, 1infection detected)
I am very new in data analysing and coudn´t find a satisfying answer in the Internet.
Thank you very much and have a pleasant day!
Sincerely yours,
Alex
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Thanks for your answer. I will Look it Up for spss. For the Competing Risk setting i think i will try an R package (time2event) or with Other methodes (Multi state or Landmarks).
Thank you all together for your Help!
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Why we use Recurrent Neural Networks for the design if Intrusion Detection System?
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What is the difference between the fuzzy neural network and Recurrent Neural Network
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A fuzzy neural network or neuro-fuzzy system is a learning machine that finds the parameters of a fuzzy system (i.e., fuzzy sets, fuzzy rules) by exploiting approximation techniques from neural networks.
Both neural networks and fuzzy systems have some things in common. They can be used for solving a problem (e.g. pattern recognition, regression or density estimation) if there does not exist any mathematical model of the given problem. They solely do have certain disadvantages and advantages which almost completely disappear by combining both concepts.
Neural networks can only come into play if the problem is expressed by a sufficient amount of observed examples. These observations are used to train the black box. On the one hand no prior knowledge about the problem needs to be given. On the other hand, however, it is not straightforward to extract comprehensible rules from the neural network's structure.
On the contrary, a fuzzy system demands linguistic rules instead of learning examples as prior knowledge. Furthermore the input and output variables have to be described linguistically. If the knowledge is incomplete, wrong or contradictory, then the fuzzy system must be tuned. Since there is not any formal approach for it, the tuning is performed in a heuristic way. This is usually very time consuming and error-prone.
A recurrent neural network (RNN) is a class of artificial neural network where connections between nodes form a directed graph along a sequence. This allows it to exhibit temporal dynamic behavior for a time sequence. Unlike feedforward neural networks, RNNs can use their internal state (memory) to process sequences of inputs. This makes them applicable to tasks such as unsegmented, connected handwriting recognition[1] or speech recognition.[2][3]
The term "recurrent neural network" is used indiscriminately to refer to two broad classes of networks with a similar general structure, where one is finite impulse and the other is infinite impulse. Both classes of networks exhibit temporal dynamic behavior.[4] A finite impulse recurrent network is a directed acyclic graph that can be unrolled and replaced with a strictly feedforward neural network, while an infinite impulse recurrent network is a directed cyclic graph that can not be unrolled.
Both finite impulse and infinite impulse recurrent networks can have additional stored state, and the storage can be under direct control by the neural network. The storage can also be replaced by another network or graph, if that incorporates time delays or has feedback loops. Such controlled states are referred to as gated state or gated memory, and are part of long short-term memory networks (LSTMs) and gated recurrent units.
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what is the effective treatment for recurrent Giardia lamblia ?
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Antibiotics are commonly used to treat giardiasis: Metronidazole is an antibiotic that needs to be taken for five to seven days. It can cause nausea and leave a metallic taste in your mouth. Tinidazole is as effective as metronidazole, and often treats giardiasis in a single dose.
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No, it is not merely hypnozoite activation or recrudescence as conventionally understood. This is a rhetorical question, because the answer is here:
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I am guilty of naming (41 years ago!) the plasmodial stage to which you refer, Alan, viz. the hypnozoite. Leaving aside the situation as regards P. ovale, it would seem that P. vivax recurrences can also (in addition to hypnozoites) have extravascular merozoites as their origin. It is a long and complicated story. The article for which the website is given in the question contains links to about 13 recent publications on the subject. This stuff isn't in textbooks (yet).
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I am going to conduct a meta-analysis about risk factors for recurrence of “A cancer” after curative resection. When I search literatures, I found that there are some qualifications for certain researches, like qualification on cancer characteristisc such as etiology, histological differentiation, tumor size, etc.
For example, one research study risk factors for recurrence of “xx-related A cancer” after curative resection, other study that for “stage II A cancer”, and someone for “large size A cancer”.
I wonder if these qualified studies can be included? I think they may increase bias of meta-analysis, but if I exclude all studies with any qualification, few available studies will left. Is there any solution to this dilemma? Thanks!!!
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7-year-old boy with standard B-ALL with late marrow and bone relapse (27 months after finishing treatment).
After second induction he had MRD < 0,01% in marrow and bone evaluation with 0,03% of blasts (79% of viability).
After second cycle 0,01% blasts in bone.
He only has related haploidentical donor.
Which is the best approach: Chemo/radiotherapy or SCT?
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Recurrent chalazia are seen to harbor adenocarcinoma clinically and on histopathology examination 1.
1. Ref: Shah SIA et al: Concise Ophthalmology Text & Atals. 5th ed. Param B (Pvt.) Ltd. 2018: 27-31
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A very good question. Recurrent chalazion is a risk. However patients under immunotherapy ,recurrent ulcerative blepharitis and with other malignancy or known to harbour the risk should be kept a close eye on. And another practice that should be commissioned is -Every piece of tissue excised from the body mandates a histopathology with proper technique of biopsy.
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Few patients have recurrent ingrown toe nail problem leading to paronychia.
Despite lifestyle management, oral/topical antibiotics, anti-inflammatories and local hygiene, the problem seems to recur.
What makes matters worse is that despite toe nail removal the problem persists when the nail regrows in a similar fashion.
What are the ways to deal with it for a more permanent solution?
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maintain local hygiene and wear proper shoe
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I wanna know about other genetic test different than Oncotype and Mammaprint, that have similar function in evaluating oncogenes and risk score, but as generic test
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Dear Daniel:
Good question!
There are six leading, and validated, genomic assays in current use in oncology:
  1. Oncotype DX (by Genomic Health) [21-gene]
  2. EndoPredict (by Myriad Genetics) [12-gene]
  3. MammaPrint (by Agendia) [70-gene]
  4. Prosigna (by NanoString) – [50-gene] Note: this was formerly called “PAM50”
  5. Mammostrat (by Clarient Diagnostic Services) [5-gene]
  6. Breast Cancer Index (from bioTheranostics) [11-gene]
and although all have some market currency, the first four – Oncotype DX, EndoPredict, Mammaprint, and Prosigna – remain the most widely deployed, with Oncotype DX being the market leader.
Constantine Kaniklidis
Director, Medical Research, No Surrender Breast Cancer Foundation (NSBCF)
Oncology Reviewer, Current Oncology
Member, Society for Integrative Oncology (SIO)
European Association for Cancer Research (EACR)
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With recurrent major depressive disorders there are some guide line about chronic therapy with antidelressants. The question seems to me less clear in the case of recurrent anxious disorder.
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Maintenance therapy (even for ever) on appropriate dose of antidepressants for e.g. PTSD or GAD is an alternative to keep a person functioning with an enhanced quality of life.
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Which is the single most important prognostic factor that most significantly influences the risk of breast cancer recurrence?
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If you have to pick only one factor, I would pick nodal status. Obviously tumor size, surgical margin, and HER2 negative status would all be other important prognostic factors.
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there are different kinds of neural networks. MLP, RBF, LSTM, recurrent, ...
I have to approximate a dynamical system with neural network, which type of NN is more suitable for this task?
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Dear Ali Namadchin,
The type of network to use depends on the type of problem to be used: continuous or discrete dynamic system, linear or non-linear. Could you send me your email to send you some information? .
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What might have caused the devastating flood in Kerala?
Is it natural or man made or a mix of both?
What lesson should we human learn from this to help future non-recurrence of events of this magnitude?
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Big or small, if a dam is not properly operated and maintained, it will be disastrous to the people and the property in the downstream region. We have noticed the destruction of a plantation area, eroding the precious soil layer along with the cardamom plants and making the whole region into a bald hillock (without any soil layer in Idukki region in Kerala) due to the collapse of a small check dam constructed across a small stream for agricultural purpose. Big / medium or small dams are built based on various parameters including the purpose & possibilities. By building a dam there will be losses and gains, and that's why EIA (Environmental Impact Analysis) will be conducted and cost-benefit ratios will be determined before finalizing a project. The operation policy of a dam is designed taking into consideration the long term rainfall pattern in the region.
What happened this year in Kerala was an unprecedented rainfall with respect to intensity and duration which led to water logging/flooding in the low lying areas. But the magnitude of the flooding got multiplied, when the water was released from many dams simultaneously. Off course, all other factors like urbanization, deforestation, etc., have also contributed in their own way to the destruction due to flooding.
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I am studying synchronization of a network of identical Hindmarsh-Rose neurons.
Please anyone tell me how to define recurrence functions of 3*10 equations in Mat-lab.
please follow the attach file.
source of the file
B.K.Bera, D.Ghosh and M.Lakshmanan , PhysRevE.93.012205
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Hi. Are you required to use this numerical integration method exactly ? Where did this limitation came from? Should you consider embedded ODE solvers of MATLAB, or construct algrebraic equations on yourself?
Below you can find a simplest Euler method code in C-like syntax for Hindmarsch-Rose model. It is possible to create any RKF method from here.
x[0]=x1[0]+h*(x[1]+a[0]*x[0]**2-x[0]**3-x[2]+i);
x[1]=x1[1]+h*(a[1]-a[2]*x[0]**2-x[1]);
x[2]=x1[2]+h*(a[3]*(a[4]*(x[0]+a[5])-x[2]));
Where x[3] is the state variable array, x1[3] is the array of state variables values on the previous timestep, a=[3, 1, 5, 0.01, 4, 1.6] is the array of system parameters, and h is the integration stepsize.
In case you need to use embedded RK4(5) ODE solver in MATLAB, just rewrite the right-hand side without "x1[n]" and "h*" members and apply the default MATLAB solver.
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Breast cancer recurrence prognostic factors
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Generally positive lymph node status and lymphatic vessel invasion consitently correlate with poor disease-free and overall survival in breast cancer patients.
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The number of malware continues to increase dynamically and are very complex and sophisticated. Distributed Malware contributes to loss or privacy invasion, having negative impact on confidentiality, integrity and availability of private data.
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Hi,
your problem of the signature based is about the management of large  database because the number of malware continues to increase dynamically, may that has a new signature, I think we will create a method to classify it, and  fast retrieve malware of  database, and since the size of database increase,, to solve classify database by using the concept of room based, we use this concept “room based” to manage the database. Each room based that has content Prohibition privileges of signature based on malware files, or pattern of collections of  signature based of malware files.
thank you
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Trying to find the scoring method for this measure, but neither the original paper nor other studies mention the exact scoring.
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It is a very interested question
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Does the adaptive recurrence have the same role in life and pharmaceutical sciences as the trigonometric functions in technical sciences ?
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Many things in real life must be done in periodic way.
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I want to know any research on recurrence interval of landslide damming on Indus through Himalayas of Pakistan.
Regards
Ijaz
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Dear Ijaz,
I have conducted an extensive research related to landslides and related mass wasting processes along the Upper Indus River Northern Pakistan. I am attaching here my article on landslide dams in Northern Pakistan for your reference. Rest you can visit my profile to take a look at other relevant pu