Science topics: Psychology, ClinicalSex
Science topic

Sex - Science topic

The totality of characteristics of reproductive structure, functions, phenotype, and genotype, differentiating the male from the female organism.
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Dear all,
I need advice for sexing doves by PCR. I have used the 2550F / 2718R and P2 / P8 primers, but they only amplify a minority of the samples. Could you recommend other primers that work for Streptopelia turtur? I have read something about the CHD1F / CHD1R primers (https://journals.sagepub.com/doi/full/10.1177/1040638716675197), although it seems that they did not work well: " Two species from Columbiformes, namely Streptopelia turtur and Streptopelia senegalensis, were also studied; However, no results could be obtained for either species because of the unsuccessful PCR amplification using CHD1F / CHD1R and 2550F / 2718R first sets, respectively. "
Any help is welcome!
Thank you very much =)
Jaime
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How are the doves being observed? How are the specimens being collected for PCR?
Personally, sexing birds and mammals that are not sexually dimorphic just happens to be my super power. Weird, huh? But that has been acquired through decades of field study. I can look at pictures of a coyote from my game cameras and it is simply obvious by their body language, or a "feeling" as to whether that is a female or male. Ravens, pigeons, geese, roadrunners- studying their behavior and subtle physical clues; they simply carry themselves in a "girl" or "boy" way. Animals never lie. That is strictly a human trait. Like that man, Lia Thomas; what a liar and a cheat!
Anyway, you are obviously a geneticist, but try discerning behavioral clues. Sorry I can't be of more help.
Good luck, Huntnlady
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Hi,
I'm working on a data analysis project for a big company. Basically we send various surveys to workers to assess work-related psychosocial risk factors in the workplace.
I need the samples to be representative of the population by sex, age and department but I have no control over which workers respond the surveys initially or in what order.
How can I test if the samples I'm getting are representative of the population?
Thanks
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Hello Sergio,
If representativeness by attributes such as age, department, etc. is vital to your study, you would be wise to use stratified random sampling (using the levels of the target attributes as strata in your design).
While you can never guarantee, short of using a complete census sample, perfect representation of the target population in your sample, what you can do is check for identifiable attributes to see if your sample is substantially different from the population. For example, if you had company-wide values for years of job tenure, you could compare whether your sample had a mean tenure that was comparable, via a significance test. If a suite of such comparisons all failed to identify salient differences, you could be a bit more confident that your sample wasn't wildly different.
Good luck with your work.
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How can one do case-control matching to randomly match cases and controls based on specific criteria (such as sociodemographic matching - age, sex, etc.) in order to improve data quality?
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Thanks.
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good day! I am a currently researching about epigenetic variations (Mainly on DNA methylation) of a fish that can change habitats (from saltwater to freshwater), my main goal/topic is about osmoregulation and my target tissue is gills. my original plan was to get samples from both habitats regardless of what sex of the fish is. however, most of the literatures that I have been reading collected male only fish. I would be very grateful for your insights.
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The male tiger pufferfish showed higher methylation level (66.009%) than in female (65.027%) and the pseudo male showed an increase in methylation level (65.945%) which is close to methylation level in male after low-temperature induction. This phenomenon was also observed in Chinese tongue sole and tilapia.
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I have manually identified each individual captured in my photos to species, age and sex on excel. How do I exclude non-independent capture events?
A. would it be an average for each demographic class for the 30 min (or how ever long <30 min, that species lingered in front of the camera)?
B. if not A, in the case of herds, would you use the photo with the maximum number of individuals?
Can this be done in R studio or must it be done manually on excel?
TIA
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It all depends on what you want to find out!
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Hello All,
First I want to start this discussion by thanking you for helping me with this as it is something I have been struggling to figure out.
I picked up this study from a graduate student before and now I am stumped by how to run the data using either SPSS or R (preferably R) or another recommendation. I have attached the data I am working with and as you can see it is divided into three treatment groups: Saline, 10mg/kg, 20mg/kg. By Sex: Male & Female and across three time periods: 12HR, 24HR, 36HR. I was looking at withdrawal scores and in particular certain attributes associated with withdrawal in the rat which is 11 items. What would be the best method to run this data if I want to look for effect of treatment and sex differences on the withdrawal behaviors?
Thank you again.
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2-Way MANOVA? As long as your dependent variables are gaussian normally distributed and have equal variances between groups.
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I am trying to perform a two-way ANOVA using SPSS with Sex (M/F) and Social Status (Rich/Poor) as factors to see if these groups are statistically different on physical attraction (this is a hypothetical example, not the real case). Here are the groups:
  1. Rich Men
  2. Rich Women
  3. Poor Men
  4. Poor Women
The result shows that the means of all four groups are different. How can I tell if these differences in means are statistically significant? Please note, the main variables of Sex and Social Status are not significant, but their interaction is significant. Can I run a post hoc comparison on these four groups? When I run Tukey, it gives an error that "post hoc tests will not be performed for this factor because there are fewer than three groups." Thanks
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Mohib Rehman, if you want to compare the four groups, you can alternatively create a categorical variable including these four groups through a recoding procedure based on the combination of sex and social status. You can then regress physical attraction on this categorical variable. The way you do it is that you run three regressions for each of which you use a different level of your categorical variable as a reference group. That should provide you with all the pairwise comparisons you need. Bear in mind that these comparisons will be considered liberal. Thus, you may also consider applying an adjustment.
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Hello all, I am analyzing body mass between small mammals and testing if it varies between sexes, reproductive status (breeding/non-breeding), and season (wet/dry). Would it be alright to have one variable called reproductive class as a factor with 4 variables: reproductive male, reproductive female, non-reproductive male, non-reproductive female. Is it possible to then use this in a linear model like body mass ~ reproductive class * season ?
Does sex and reproductive status have to be separate terms?
Additionally if I have significant differences between males and females, can I group the two sexes together to see if body mass overall changes with season?
Thank you
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Hello Kyle , you can only estimate a linear regression between body mass and reproductive class, if the reproductive class is a continuous variable, otherwise you cannot use it. As for the test of difference in body mass during the season, it is interesting. But I advise you to do the test comparing male and female in each season (t-test), it would be more interesting. Hope this helps
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what are the opportunity of determination of embryo sex in the fertilized egg of chicken
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In the chicken and other birds, sex is determined by a ZZ:ZW sex chromosome system. ... While the genetic trigger for sex determination in birds remains unknown, some promising candidate genes have recently emerged. The Z-linked gene, DMRT1, supports the Z-dosage model of avian sex determination Rafea M.T Khaleel
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Greetings,
We have been conducting a retrospective cohort study. The variables we are examining are assumed to be very age-dependent and the exposed population is very small (~40 patients), therefore we have considered matching for age and sex at a 1:2 or 1:3 ratio to increase statistical power and limit confounding.
Which statistical test would be most appropriate for calculating risk ratios for dichotomous categorical variables?
This article ( https://academic.oup.com/epirev/article/25/1/43/718675 ) suggests conditional Poisson regression, which I have attempted in Stata, but it appears to work only for 1:1 matched pairs.
It also suggests an adjustment of Cox regression so as to yield the same results as conditional Poisson regression (" if the time to death or censoring is set to some arbitrary constant value and if the Breslow or Efron methods are used to account for tied survival times, the results will be the same as those from conditional Poisson regression, as the likelihoods for these methods are identical when the data come only from matched pairs ").
I have recently attempted a similar adjustment (as described here: https://www.ibm.com/support/pages/conditional-logistic-regression-using-coxreg ) to yield the same results as conditional logistic regression (odds ratio) for a 1:N matched case-control study using Cox regression.
If such an adjustment is possible, how exactly could it be implemented in SPSS? If not, what other alternatives are available to us in this juncture?
Thank you in advance.
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Maybe not useful for the original question anymore, but still good to know: a very detailed exposition on how to perform a matched cohort analysis is present in Kleinbaum's Logistic Regression 3rd edition. It gives the specifics on how to do it in SPSS using the Cox regression module in the Appendix. I used it some years ago, and it works (obviously). Hope this helps.
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I plan to use BALB/c mice for studying pneumonia caused by the mouse-adapted influenza virus (A/PR8/H1N1).
I want to know, what is the requirement to determine the sex of an animal? ​It seems that most of them choose female mice, but there are also researchers who use half male and female mice. What is the difference between them? Does the sex of the mouse have a significant impact on the final result?
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One of the main issues using male mice (and housing several in the same cage) is that they tend to fight with each other. If this occurs, they need to be separated and placed into individual cages. This could certainly impact the results of your study.
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In my g power ap which linear multiple reg should I tick? One says R squared deviation from zero and the other says R squared increase. I'll be doing a Hierarchical multiple regression analysis with a health beliefs questionnaire as my dependent variable and I'll have several independant variables (categorical and continuous) such as sex, education level, and the response to several different questions, and scores on a characteristics scale.
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Thanks for your help Christian and Remal
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Hi,
Can somebody share statistics by countries how many people died or affected, their sex, age and etc.
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As to guess from the marketing and its problems side, Astra Zeneca vaccines are the worst at present. But only longer time will show the reality.
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1) In short, do you need to check for multicollinearity when you only have one continuous independent variable (but several dichotomous independent variables)? I have been told by a statistician that i don't. However, how should I handle close association between two binary independent variables in that case?
2) A follow up/more detailed question on difference between adjusting for a covariate and doing stratified analyses
I am using logistic regression to evaluate the association between a binary independent variable of interest ("disease A") and a binary dependent variable ("Condition x"). I have three candidate covariates I wish to adjust for since they have a recognised influence on the dependent variable (age in years, sex and origin). Thus, I have four independent variables 1. "Disease A" (binary) 2. Years (continuos) 3. Sex (binary) and 4. Origin (binary). When I run the logistic regression model, I find a significant association between the IV of interest ("disease A") and the DV ("Condition X").
However, I would like to see if the association between Disease A and condition X is driven by comorbid "disease B".
Here I see two solutions: 1) Two separate analyses on subgroups i) disease A without comorbid disease B and ii) disease A with comorbid disease B or 2) Add disease B as a new independent variable in the logistic regression model.
In subgroup analyses, disease A without comorbid disease B has a small sample size, and type 2 error cannot be ruled out/is likely. Whereby I would like to adjust for "disease B" as an independent variable in the logistic regression model. However, disease A and disease B are highly associated/have a high pairwise correlation. (In VIF diagnostics VIF 2.5 which is the lowest threshold to raise concern about multicollinearity, however, I only have one continuos variable in the model) In the new model, nor disease A or disease B have a significant association with the dependent variable. I believe this is due to the high correlation between disease A and disease B, and that they somehow mask/over-adjust/attenuate each others effect?
I would be very happy to hear your thoughts on this,
Martin
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There's no reason why collinearity can't be problematic with dichotomous IV's.
As an initial step, it makes sense to determine the correlation among your IV's. And also between your DV and each IV. Since your nominal variables are dichotomous, these can just be coded as 0 and 1 and you can use Pearson correlation or Spearman correlation.
In theory, you should be able to include all relevant IV's in the model, perhaps also with interactions between various IV's. However, there are several reasons why this model may not be ideal. One reason is having high degrees of correlation among your IV's. Simple correlation between any two IV's may not reveal this collinearity (which could be among multiple variables together), but it's a start.
From your description, it sounds like you've identified the problem: Disease A and Disease B are correlated. Adding them both in the model results in neither being identified as a significant factor. But one (or either) is identified as a significant factor if included in the model without the others. This is the nature of multiple regression.
I'd say that with the data you have, there's no easy way to disentangle the effects of Disease A and Disease B. You can include one in the model or the other. If they're correlated, there may not be much use to including both in the model. In some ways, having those individual correlations of each IV to the DV help the reader to understand the data and potential implications.
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Your research is very interesting. I think that studying it in terms of gender also gives new results because the feelings of women and men are always radically different from each other. Expressions of crying, fear, and affection are observed differently in both sexes.
Have you done any separate research on gender issues related to your topic?
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Actually I will have to... Topic of my habilitation is Education of woman in the developing countries, way to improve the environment...
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Hi all,
I'm analyzing a set of longitudinal data obtained from subjects at regular time intervals following an intervention. I want to look for the effect of time, but I also want to adjust the observed time effect for age and sex of subjects. My initial thought was to run a linear model like this:
lm(Signal ~ SubjectID + Time + Age + Sex)
However, age and sex are both attributes of subjectID, and, therefore, are not independent covariates. Should I rather simplify the model like this:
lm(Signal ~ SubjectID + Time) ?
Any suggestions are much appreciated.
Thank you very much!
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You need somthing more than standrad linear models to analyse longitudinal data. You dependet variable whill be time varying but gdsex wil be time invariant and applt to teh peson. The natural method of analsyis is the two level random effcets model will can hadle within individual and between individual variation.
This is a short practical example of what can be done
this is an overview of alternative models
this is a comprehensive site that considers a range of software :
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SRY gene is responsible for the initiation of male sex determination in humans. Can the presence or absence of this gene alone be enough to conclude the sexual phenotype of a fetus. Can gender be determined before 14 weeks of pregnancy?
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This is a fascinating subject and one that I have studied for many years in human and XY Sex Reversed horses. To summarize our findings, indeed in most cases, sry is the testis inducer which can trigger the pathway that leads to suppression of the female pathway and the devlopment of the male. Interestingly I have collected large pedigrees of horses that carriy the SRY mutation. This included XY females with gonads that range from a fertile overy to gonadal dysgeneisis to ovotestis to abdominal or inguinal testes. The animals are also siblings to a few tall, virilized XX females with abdominal testes and SRY present. Clearly these cases arise by abnormal ectopic recombination in male meiosis. Most intereting is that those XY mares that are the most viril with testicular tissue present have deletions limited to SRY ONLY. As the more femenine cohorts develop fertile ovaries and manifest large deletions that removes SRY and regions both 3', and 5', of tdf. XX virilaized females are SRY positive with a testis in the abdominal position and clitromegally. Finally we are very initerested in those animal with testes that are SRY negative. and are currently studying to role of SOX9 to act as a sort of backup inducer mechanism.in dire situations.
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I am examining the interaction effect of Sex (male, female; between-subject factor) and Time (11 levels; within-subject factor). I would like to adjust for a time-invariant covariate.
In the 'model' tab, which model effects should I add? For example, Sex, Time, Sex*Time, Covariate, Time*Covariate?
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I think it is better to add all possible interactions to your model
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I have a sample composed by 14 Patients and 13 Healthy control. In total, there are two independent (between) variables (named Sex and Diagnosis) and a dependent (within) variable (composed by 6 emotion).
A non-parametric ANOVA (Kruskal Wallis test) was performed to analyze the effect of the “emotion” variable when controlling for “sex” and “diagnosis” variables.
I think that it is not appropriate performing a Repeated measure anova due to the sample sizes (considering also the two variables between: diagnosis and sex).
Moreover data the data are not normally distributed.
Is it the correct way?
Moreover, Where interactions are significant, can I go into details with a post-hoc analysis (Bonferroni)?
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It's not clear how the dependent variable is measured. Was each participant measured twice (pre/post) on a single emotion scale? Or was each participant measured twice on six different emotions? In other words, is there one dependent variable or six?
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White Privilege and Multicultural Counseling Competence: The Influence of Field of Study, Sex, and Racial/Ethnic
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I'm performing a study concerning the risk of fall in hospitalized patients. I read a lot of literarure about the importance of matching for confounders such as sex and Age, but what if in my case there are a lot of works reporting age as an important fall-risk factor, and for this reason I want to include it in my logistic model? Wouldn't matching create a bias by flattening the effect of age on the falls phenomenon?
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Currently I am doing a systematic review of the predictors of clinically significant weight gain amongst those who take antipsychotics. This is usually defined as a 7% or more increase in weight. My inclusion criteria will allow for both RCTs and non-randomised studies of interventions. My question relates to distribution of predictor variables. My understanding is that randomisation will allow for even distribution of potential confounders and baseline characteristics of participants. In an observational study, this will likely not be the case. My question relates to whether I should be prioritising RCTs over non-randomised studies of interventions because of this. As regression analysis will just be conducted amongst those who are treated with the drug, will baseline data, some of which might function as a predictor variable e.g. sex, be more evenly distributed if the sample arises through a randomisation procedure or not?
I may be over-complicating this, as if I include for sex in the model - perhaps if accounts for this even if males vs. females are not evenly present within the sample?
Any help much appreciated!
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Dear Ita,
From a technical standpoint, randomization is a requirement for the probabilistic interpretation of the regression model, not the fitting of the model. The purpose of the randomization is to reduce the influence or biases of the researcher in selecting a sample so the sample will fit the probabilistic model, and hopefully, resemble the population. While using nonrandom data does violate an assumption for a pure probabilistic interpretation of the results, it is seldom achieved completely even in published studies. When that is case, the nonrandomness of the data should be included as one of the limitations of the study so consumers of the research can make their own judgments about the fidelity of your results.
Good luck,
Jim
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Literature starting appearing from case histories around the world as we struggle to accept that this incredibly virulent virus appears to have different host receptors interms of who gets hospitalized/passes away (rip). i CAN CALL IT A VISCIOUS LITTLE BASTARD AS IT NERALY KILLED ME IN Feb lol
However my sexual health clinic is submitting a truly fascinating case, quick summary
Heterosexual couple,wife presented with corda equina symptoms,this was ruled out but syphils serology was positive for late latent syphilis,only thing in her sexual history was casual hetero affair 4 years previously. Absolutely denied ANY UPSI including oral,vaginal etc. Her CSF was negative fro T.Pall and MRI brain vlear for M.C. She was referrered to us for treatment. HOWEVER before she got to us she developed severe COVID. After recovering from acute Covid she had resumed sex with her husband who we obviosly asked to attend for treatment as a contact BUT HE PRESENTED WITH raised RPR positive and a chnacre i.e classic primary syphilis,
Before you all say he was having casual MSM or sex outside the marriage ! 5 Dr and nurses took his sexual history on different occasions because we were baffled and I put any money I HAVE THAT HIS HISTORY WAS ENTIRELY INNOCENT.
Our hypotheize COvid 19 reactivated her latent syphilis deeming her infectious.She remained Ign negative....
Certainly worth a publication in case other Drs are scracthing their heads
Thoughts ? This is what we have to do with such a new virus and when the HIV crisis struck it was scientists and clinicians lifting the phone to each other East to West coast US reporting on young gay men dying of PCP!
I
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This type of genetic recombination of SARS-COV-2 may be possible due to violation of Covid protocol ( like mask ,sanitisation etc ) . The ability of muiants becoming more virulant for Variants Under Investigation ( VUI cases ) to generate some secondary latent infections like HIV , Syphilis etc . https://www.researchgate.net/profile/Jaydip-Datta/post/How_the_world_is_combating_against_new_variants_of_SARS-COV-2/attachment/609d18fa3164270001daa79e/AS%3A1022977634209792%401620908282416/download/1651.jpg
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Doing an analysis on change in knowledge score over 3 points in time after training. Wants to show results for unadjusted change and adjusted for age, sex, department and site.
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@Sanjida Possibly this tutorial video would help your cause👇🏼
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I'm planning on sexing blood samples from Osprey nestlings. Where can I buy primers in the US? I'm looking for P2, P8 & CHD1F, CHD1R.
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There are lots of companies, just ask a colleague in your lab which one they have an account with already.
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I have counts data for 560 immune genes and I am trying to statistically analyse differences in these counts between Rh- and Rh+ individuals.
In my analysis sex and age are confounding factors. To correct for sex I split the group into males and females and looked at Rh+/- response in each separately through unpaired t tests in R. This gave me a p-value for each gene.
However, as age is also a confounding factor I need to create a non linear regression model to correct for age.
Can anyone advise me on how I go about doing this?
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You should analyze the effects of age and sex and Rh status on the log counts in one model. Using log counts is important toachieve linearity and homogeneity of variance (you could alternatively use a negative binomial model with log-link, but it also works with the standard toolbox using log counts; the difference is that when using log counts you will get predictions of the mean logs instead of the log means, but this is anyway not your question; bith models give you the same estimates of the effects: log-ratios).
Since the relation between age and log counts is not of primary interest and presumanbly not linear, it's advisable to use some spline basis to model age, as Holger Steinmetz already proposed (Holger: this is also possible in a standard linear model using splines::ns()).
Additionally, as David Eugene Booth suggested, you might also consider interactions, so a full modelmight look like
log(counts) ~ Rh*Sex*ns(Age,df=3)
that might be reducable to
log(counts) ~ Rh + Sex*ns(Age,df=3)
that might give a simpler interprtation of the (sex- and age-adjusted) effect of Rh on the counts.
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I am writing a short note about bats diversity in two sites (the sampling period for one site is of two days and in the other site is of six days) but besides the species and abundances, I also have information about the sexes of individuals. Any recommendations of papers or about what could I do with the sex information of the species?
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Just include all variables in your analysis and see what it shows. Also, a paper is better than a short note :)
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Is 8th week the earliest week where we can determine the fetal sex ?
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ultrasonography
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I am working on building a sex ed and relationships course for adolescent students with ASD who are part of the mainstream student population; meaning they are of average intelligence and so can complete most academic tasks. However, they still struggle with social interactions, including how to handle situations where they are attracted to someone. They also often differ in their "social age" as compared to their physical age and so regular sex ed does not meet their needs.
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Heres some references I found that may help answer your question.
Solomon, D., Pantalone, D. W., & Faja, S. (2019). Autism and adult sex education: A literature review using the information–motivation–behavioral skills framework. Sexuality and disability, 37(3), 339-351.
Sala, G., Hooley, M., Attwood, T., Mesibov, G. B., & Stokes, M. A. (2019). Autism and intellectual disability: A systematic review of sexuality and relationship education. Sexuality and Disability, 37(3), 353-382.
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in order to study of stress impact on both sexes we want to use hormonal supplementation without gonadectomy !
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that seems to be the cheaper(and more inhumane method). can't you do a chemical analysis of their secretion( and also faecal matter, urine) to contrast levels of hormones(eg progesterone in humans analogously) and compare it with a control group?
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Some papers have suggested that males are more susceptible to SAR-CoV-2 infection. I am curious to know if it could be as a result of immune differences in relations to sex, age or prior health conditions (e.g. diabetes, obesity and CVD).
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The Gender effect in the SARS-CoV-2 and COVID-19 Pandemic
The more susceptibility of males compared to females stems from the sex hormones (Estrogens, Testosterone) and single X-chromosome in males.
This can be understood by having a look at these articles (1-3):
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Dear RG staff,
I uploaded my new article titled, "Expanding our international reach: Trends in the development of systemic family therapy training and implementation in Africa." This article was published in the Journal of Marital and Family Therapy (JMFT) in March 2021. However, when I was editing the information to upload the article in RG, I accidentally clicked the "Journal of Sex and Marital Therapy." So now my profile shows that my article was published in this journal and not JMFT. Can you please help me rectify this issue and include the correct Journal in my article? I tried to edit my profile by could not figure out how to change the Journal name
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Have a look at the Help Center link you can find while scrolling down and you will find: https://explore.researchgate.net/display/support/Help+Center. If you go to Research & Publications, this might help you:
If it all does not seem to work, I advise you to contact RG support team: support@researchgate.net since is it unlikely that the RG people will see this question and do anything.
Best regards.
By the way: Congrats with what seems to me a paper in an excellent journal.
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If I want to compare characteristics of 2 groups of patients, one group is recruited from a random sample of the general public while another group is from a sample with a fixed gender (male to female 1:1) and age group ratio (equal number of subjects in 6 different age groups) from the public. Is it still possible to look into the effect of age and sex in such comparison? if I want to examine effect of other behavioral factors, what statistical test can be used to minimize effect of biases arising from two different sampling methods? Thanks!
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I'm afraid that you cannot compare the data from these two samples....
The research sample design criteria should be the same, at least, when the respondents are different.
You can describe the findings from both surveys, but only as secondary data analysis.
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Hello everyone,
As part of my research on the sex business, I received answers to a questionnaire from 206 sex workers in my country. I would like to extend my research to the surrounding countries, also within the framework of possible cooperation. However, organizations that have access to sex workers don't want to cooperate.
Do you have any idea how to proceed, how to get to the research sample of sex workers abroad (I understand that it is very difficult)?
Is there anyone who has done similar research and might be interested in collaborating?
Thank you and have a nice evening
Stefan
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Dear Umut Can Öztürk!
Thank you for your answer. In my country, I have already managed to get 200 answers to the questionnaire. I would like to extend the research to other countries. I am looking for authors from other countries to publish together.
Best regards
Stefan
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What are the impact of age,sex,co morbidities and clinical symptoms on severity of COVID_19 cases ?
COVID_19, Age,Sex ,Co morbidities ,Clinical symptoms.
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I'm going to do a brief research about the dependance between the number of cases of Covid-19 and sex( male/female) . One of the requierements is to use inferential statistics to test the hypothesis, but I haven't been able to find a type of statistical analysis to test if this two variables are related. I could really use your suggestions.
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The role of sex in disease pathophysiology studies is a emerging area to be addressed. Could someone share thier experience regarding this issue? How much difference did you see in your studies between male and female participants or subjects or animals?
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The Future of Research on Biological Sex Differences: Challenges and Opportunities
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I've got some protein lysates which have been stored at -80 for many years. I'd like to determine the sex of the mice these samples came from, however this is all I have from them.
Is there likely to be any DNA in the protein lysate that I could extract for PCR? And does anyone know of a method I could use to do extract this? (I have tried a PCR with the lysate, but did not see anything when run on a gel)
In theory I could determine sex by western blot using a protein expressed from a Y-linked gene, however I'm not sure of any that would be highly expressed in my samples.
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Hello, depends on which technique used for protein lysate preparation it may contain DNA maybe not due to degradation. But you can try to check.
You can use the Benzyl chloride method for DNA extraction which quite well for isolation of DNA from high protein-containing substances.
Good luck!
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I am interested in identifying sex-specific survival rates. I have a long-term dataset of individually marked birds from one breeding colony. Of these, I am able to genetically sex a subset. Additionally, I have access to mark-recapture data on nearby breeding colonies and so can estimate emigration from my target breeding colony. However, I am only able to sex individuals from my focal breeding colony.
My research project addresses both survival and dispersal (permanent emigration). So I'd like to disentangle these as much as possible and look at effects of sex. One approach is to only model the subset of birds I'm able to sex. Another approach would be to model all individuals, including unknown sex, and apply the techniques in Nichols et al. (2004) to apply probabilities of "male or female" to unknown-sex birds (I may be summarizing that poorly).
Can I use a multi-site model if I only include individuals marked on one colony, but do have data for permanent emigration to other colonies? Would it make sense to use a multi-site model with all individuals from all colonies but only have sex information from one colony?
Thanks in advance!
Nichols, J.D., Kendall, W.L., Hines, J.E. and Spendelow, J.A. 2004. Estimation of sex‐specific survival from capture–recapture data when sex is not always known. Ecology 85(12):3192-3201.
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May be you must used a multi variate analysis bu using your data about breeding colony and inserted nearby breeding colony as supplementary data.
Also, if you can estimate migration between colony, you can added this parameter as factor to your analysis.
Good luck
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A mathematical model of temperature-dependent sex determination
of crocodile eggs
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Thanks for your feedback
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I need to calculate CFR based on age group and sex. For this, I need daily COVID-19 confirmed cases and deaths according to age and sex for Sri Lanka. Where can I find this?
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Teresa K. Duez, unfortunately there was no age and sex-specific data. Thank you for your suggestion though.
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I need to clarify some important points that lead to sex change in fish, especially from Cichlidae family
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I following the best answer.
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Please, can any one help me with publications on the amino acid profle of sex reversed Nile tilapia?
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I following the best answer.
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Dear all,
I am working on an assignment trying to evaluate the relation between 5 variables (age, sex, mutation, biomarker1 and biomarker2) on time of death.
I have constructed a model including only complete cases with complete.cases function
data_new <- with(my_data, my_data[complete.cases(sex, age, mutation, BM1, BM2),])
fit.fullmodel <- survreg(Surv(TimeDeath, event) ~ age + sex * (mutation + ns(BM1, 3) + ns(BM2, 3)), data = data_new)
summary(fit.fullmodel)
I have considered the above stated model as my full model and now I want to evaluate if I can drop some complex terms with an omnibus test.
fit.fullmodel <- survreg(Surv(TimeDeath, event) ~ age + sex * (mutation + ns(BM1, 3) + ns(BM2, 3)), data = data_new)
fit.reduced <- survreg(Surv(TimeDeath, event) ~ age + sex + mutation + BM1 + BM2, data = data_new)
anova(fit.fullmodel, fit.reduced)
As output RStudio returns: Error in forms[2, ] : incorrect number of dimensions.
I don't know what it is wrong. How can I solve this?
I doens't matter if i just try to simplify the model I still keep on getting the same error.
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try [2. ]
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Hi,
I am currently conducting my dissertation research looking into the impact of level of LGBTQ+-inclusivity of school-based sex education received upon bisexual individuals' self-image and relationship satisfaction later in life.
My IV (inclusivity of sex education received) will be self assigned by the participants when they complete the questionnaire and I will have 3 DVs (level of internalised biphobia, sexual distress and relationship satisfaction).
Please can someone offer some advice on how best to analyse the data?
Many thanks for your help!
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First, a quasi-experimental design requires a comparisons between those who were exposed to some factor versus those in a "non-equivalent control group" who did not have any exposure to the "intervention." So, that may not be the more accurate description for what you are doing.
Practically speaking, if your analysis is basically a comparison of three different groups, then a one-way ANOVA would adequate. You could do that separately for each DV, or you could combine them into a MANOVA.
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I want to know if the sex factor influences bullying
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OBVIOUSLY YES ... in fact, "sexual harassment" is a subtype of Harassment, more frequent - to the shame and disgrace of men - among men - not all - towards women; Thus, one is not more manly, nor brave, nor "macho" if not more rude, offensive and aggressive "primate"
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I need to calculate CFR based on age group and sex. For this, I need daily COVID-19 confirmed cases and deaths according to age and sex for south Asian countries, i.e., Bangladesh, India, Sri Lanka, Maldives, Afghanistan, Nepal, and Pakistan. Where can I find this?
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Alexandr Chernov, Md Alimul Haque, thank you for your replies. But I afraid there's no information regarding age and sex in these sites :(
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Hey all,
As part of a current study dealing with the fish fauna in the Baltic Sea, we have collected specimens of the snake blenny Lumpenus lampretaeformis. We would now like to determine sexual maturity stages for this species and are thus looking for reference guides (e.g. visual/descriptive material) or someone who has any kind of experience determining sexual maturity stages (or sex, if no eggs are visible) for this species. Until now we didn't find any scientific publications on this topic. Would be great if someone could point us in the right direction :)
Thanks a lot in advance!
All the best,
Michael
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Unfortunately the Fishbase data is based on n = 1 which is not much use to you. Given the lack of data for this species, I think you are going to have to use visual identification of gonadal development to answer your question. I would point you to the following offering from Youtube as a starting point!
Good luck!
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This post is going to be a little long, so I apologize in advance for bothering you. I learned that quality control is super important so I want to make sure that I do this right.
I have several questions for my QC pipeline but hopefully nothing complex. I am just having some hard time to find the right commands. and when I do I am not sure of the inputs.  Note that I am using plink v1 (because all the examples I found use plink v1) so if you can give me the v2 version that would be helpful but it is not my main concern. Note also that I am using data from UK biobank so every chromosome is in  separate files (genotyped: .bed .bim . fam  / imputed: .bgen .mfi .sample)
My pipeline is based on 2 parts :
1- per individual filtering
      a. Removal of individuals with excess missing genotypes       b. Removal of individuals with outlying homozygosity values       c. Removal of samples showing a discordant sex       d. Removal of related or duplicate samples       e. Removal of ancestry outliers
2 - per sample filtering
      a. Removal of SNPs with excess missing genotypes       b. Removal of SNPs that deviate from Hardy-Weinberg equilibrium 3. Removal of SNPs with low minor allele frequency       c. Comparing minor allele frequency to known values
My code:
1.a: ./plink --bfile ukb_cal_chr{}_v2_reduced --missing     --out  output1 comment: use R to remove individuals with missingness >.05
1.b: ./plink --bfile ukb_cal_chr{]_v2_reduced --het             --out output2   comment: use R to remove individuals with the absolute value of F >.05
1.c: ./plink --bfile ukb_cal_chr{}_v2_reduced --check-sex  --out output3   comment: not sure what the input is ? then in the output remove individuals with status =problem
1.d: ./plink --bfile ukb_cal_chr{}_v2_reduced --genome --min 0.05 --out output4 comment: using R in the output  output4.genome, for every pair remove the one with the lowest genotyping rate (unless there is a command for that  in plink ) (is that right?)
!!! However, I found that --genome takes too much time, is there another way?
1.e: ..... comment:   I found this command : 
plink --file data --cluster --neighbour 1 5
comment: but I am not sure what it did and how to use the output to filter the individuals and what  the input file is (file or bfile)
2 - a,b,c : ./plink  --bfile input  --maf 0.01 --hwe 1e-6 --mind .1 --geno .1 --make-bed --out output
That's it for my pipeline. my main questions are related to the red parts, so just 3 questions. Also, if you found errors in my pipeline can you please correct me?
In conclusion here are my 3 questions:
- since I have one file for each chromosome, is the input of the command 1.c , the chromosome X?
- the command -- genome takes a lot of time, is there a way to speed it up or to estimate the relatedness of individuals in another way?
- I am still not sure how to filter ancestry outlier using pca?
Can you please help me? thank you
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Dear Emile Dimas
first Q:
plink --bfile data --missing-genotype N --make-bed --mind 0.05 --maf 0.05 --geno 0.1 --hwe 1e-6 --recode --out gwasclean
Your way of QC is correct.
second Q:
It is better to use GCTA and split the calculation by applying --thread commend or calculate --grm for each chromosome and finally merge them together in an individual file.
third Q:
there is a way to put a couple of first PCs according to their eigenvalues as covariates in your analysis for adjustment and lowering inflation rate.
Regards
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Hello. I'm trying to find out when the demographic question of Male or Female shifted from Sex to Gender. Focused on the West (US and UK) primarily. Any knowledge? It seems that in the 1950s US census asked about sex and then in 1970s it changed. It's a very vague mention in an article. I wondered if you have any more specific knowledge or suggestions? Thank you.
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The concept of gender emerged in the 1980s as a prominent term used in the feminist lexicon. Where it appeared in North America and then Western Europe in 1988.
First I refer you to the links
paper
^ Haig, David (April 2004). "The Inexorable Rise of Gender and the Decline of Sex: Social Change in Academic Titles, 1945–2001" (PDF). Archives of Sexual Behavior. 33 (2): 87–96.
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Hello,
I have 2 between-subject Factors (generation and judger gender) and 2 within-subject variables( Actor gender and Target Gender) as an independent variable.
So all independent variable is 2 levels.
First of all, I am considering three-way ANOVA, Repeated Measure ANOVA, Repeated Measure ANOVA after split file.
The actors made the targets, the judger rated this target and the target gender is the target's sex.
My first research topic is that what's the differences in the generation and the judger gender on DVs, and The second is how actors and judger's ratings differ depending on gender. In other words, how do male judgers rate man-made men and woman-made men differently? I expect a male evaluator to give a higher score to a man-made man than a woman-made man.
One of the problems I think is too many factors when I conducted 2 between factor and 2 repeated measure variables as IV.
If 'generation' is the A, 'judger gender' is the B, 'actor gender' is the C, 'target gender' is the D, full factorial model has so many terms; A, B, C, D, A * B, A * C, A * D, B * C, B * D, C * D, A * B * C, A * B * D, ....., A * B * C * D.
Or... do I select terms I want in the full factorial design?
The Alternative is to conduct a two-way repeated measure ANOVA after split file by generation (include 2 within-subject variables).
But if the generation effect is significant..?
The other alternative is to eliminate context who manipulate the target as yield simple mean by the target gender.
The last of my tactic is using the three-way ANOVA to made new between-subject independent variables; Target gender.
How about this idea? Is it proper statistically?
I would like to hear your valuable opinions.
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Hello again, Inhae,
Do you mean you're invoking a Bonferroni-adjusted p-value threshold for declaring statistical significance due to the large number of tests to be evaluated? If so, that lowers, not raises, statistical power.
Familywise type I risk level is a legitimate concern when running a bushel basket full of tests. However, some folks believe that, as long as all effects tested are independent of one another (which would be true in a balanced design, for example; or with purely orthogonal effects in general), that the "family" concept doesn't really apply (use of the same error term for multiple tests would appear to negate this argument for me).
In general: (a) always choose risk levels when hypothesis testing that honor the consequences of being wrong; and (b) explain the rationale for your choices.
Though most published studies and virtually every statistics text book would have you think otherwise, I like the perspective of Rosnow & Rosenthal: "Surely God loves the .06 nearly as much as the .05." ( Statistical procedures and the justification of knowledge in psychological science. American Psychologist, 44, 1276-1284.)
Good luck with your work.
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Dear colleagues,
I have to report incidence rates for spontaneous pneumothorax, which from a study has an incidence in males of 19.3 and 8.1 in females(per 100000). I wanted to report a general incidence but should I standardize this value for sex or I can just do the mean? eg. (19.3+8.1)/2
thank you for your guidance
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Dear Andrea
Who are your studied population? are the patients presenting with respiratory symptoms or general patients attending a medical facility?
Suppose you studied 500 males and 300 females and found 30 males and 13 females with spontaneous pneumothorax
Then
IR among males =( 30/500)x 1000= 60.0 per 1000 males
IR among females = (13/300)x 1000=43.3 per 1000
IR among both gender together = (43/800)x1000= 53.8
The relative risk of pneum in males as compared to females = 60/43.3=1.4
Note: This is only basic statistical analysis
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In an article Dr. Blaire suggested that until there was contact with western societies, many societies had a more fluid notion of gender roles https://www.psychologytoday.com/us/blog/inclusive-insight/201809/has-gender-always-been-binary
I am not sure that I agree with this idea. Gender is culturally defined, and is an eidos related to sex, as I suggest in
But that does not mean that gender is likely to be fluid. From what I can see, it is not that gender roles were fluid, but rather that there are simply a number of cultures with ternary and quaternary gender divisions. For instance, many Native American tribes expressed the concept of “two-spirit” people. This established a quaternary gender system. But I don’t think that people easily existed outside of those four roles or that the roles were not well defined. It’s just that there were more of them.
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Given that cultures exist/are concentrated in certain geographical places at particular times in history, am not sure what you are implying. And, patriarchy was a Western system, not a native Indigenous one.
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Hello. Can anyone help me with some useful data for my Master's diploma? I am making an Artificial Neural Network for prediction diabetes. I know about some open data sets like Pima Indians, Austin Public Health Data set, and UCI data sets. But they did not fit my research.
I want to find some data with similar parameters: IBM, AGE, SEX, SMOKING, PHYSICAL EXERSISING, HEALTHY EATING and etc. Some data without laboratory parameters of blood. Some like Austin Public Health Data set but with IBM.
Ty for your attention.
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Hello,
- I've ran Multiple Linear and Logistic Regression Analysis in R in order to compare the effect of a couple of predictors including Sex and group for IQ (categorically defined as High or Average).
- I've been intrigued by results differences when using lm and glm functions when I :
1) the parameters remain coded as factors
2) the parameters are converted to numeric using as.numeric()
- considering there isn't any missing values
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Hello Judicaël Fassaya. You are getting different results because R handles metric and categorical variables in different ways. Regarding regression models and concerning categorical independent variables, R tests if group "b" differs from group "a" in regard to the dependent variable. In such cases the alphabetic first group is always used as "reference". On the other hand, if your independet variables are metric R tests if the dependent variable is influenced by the independent one.
In your case I would include Sex and IQ as categorical variables in the model.
I hope this helps.
Best regards Paul
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Every year, an estimated 21 million girls aged 15–19 years in developing regions become pregnant and approximately 12 million of them give birth. At least 777,000 births occur to adolescent girls younger than 15 years in developing countries.
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Education can include clear teaching methods on healthy sexual activity, and percentage on preventive barriers that can prevent pregnancy. If you cannot reach zero abstinence, provide tools to a health pregnancy and its impact on establishing a family with safe conditions
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Synthetic sex dolls are presently sold on global scale to replace human commercial sex workers in brothels, where the dolls can be used by multiple users. Can the use of these products increase the prevalence of STDs? Because, the dolls could be used by multiple users, serving as fomite for indirect transmission of STDs from an infected inanimate object to a susceptible person. Are the synthetic sex dolls safe at all?
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It could be possible if the dolls are not strelized after use. Neisseria gonorrhoae is one. Although, nonsexual transmission of gonorrhoea seems to be extremely rare. Only one case of nonsexual transmission of genital Neisseria gonorrhoeae is documented in adults', involving two patients in a military hospital who shared a urinal. N. gonorrhoeae has been shown to survive in infected secretions on towels and handkerchiefs for 20 and 24 hours, respectively.
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Is it appropriate to compare rates of (for instance) tea consumers among patients admitted to a hospital with some disease, with rates of tea consumers in general population in the same country? (Given that in terms of age and sex representation, the group of patients and general population are reasonably similar).
Would difference between (for instance) 10 % of tea consumers (with upper limit of 95% confidence interval 12%) and 20% of tea consumers in general population be convincing as evidence for a protecting effect of tea consumption against the disease?
I do understand that randomized case-control study would be much more convincing. Nevertheless, can the above mentioned be considered as evidence too?
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You ask: "Is it appropriate to compare..." I guess it depends on what you want to find out, i.e. what is the source population that you want to make conclusions about?
Example:
for instance you want to find risk factors for severe (need for hospitalisation) symptoms of COVID-19.
Among all males aged 70-79 patients submitted to a hospital with severe COVID-19 you find 40% are smoking.
In your general population you known that only 20% of males aged 70-79 are smoking.
You conclude that smoking poses and atributional risk for severe COVID-19 symptoms.
This conclusion neglects that you are only at risk of developing COVID-19 if you are infected with SARS-COV-2. The populations that you should actually compare the hospitalised patients with is not all males aged 70-79, but infact only those at risk of developing either no, mild or severe symptoms, namely those who have been infected.
To conclude my example: if, smoking is associated with a higher risk of getting infected (e.g. smokers could be from another socioeconomical class, have other movement-patterns, adhere less public recommendations on transmission prevention, more often live in urban areas...) their could actually be e.g. 40% smokers among all males aged 70-79 that are infected with SARS-COV-2, and the exposure would be equally present in the group that develops mild, and those who develop severe disease.
And just to be clear. This was an example. I do believe smoking increases the risk of developing severe COVID-19 if infected, based on what we currently know about the disease.
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In some articles it has been reported that 2 to 36 MNs were observed in 1000 cells.
Some articles have found age and sex dependency.
How much is this range if you study in the age range of employed (25 to 60 y-o)?
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Multiple studies have found that micronuclei frequency in women is higher than in men and that the number of micronuclei increase until around 70 years of age. Micronuclei levels ranged from 0.5 to 1.4% in men to 0.9 to 1.8% in women. Gender-related differences were mainly seen in younger age groups (<= 50 years) with an almost two-fold difference between men and women.
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When the condition has length > 1 in ifelse in r and there are more than 2 statements how to use ifelse?
I have a data set wherre I want to categorise people in to categories using sveveral arguments.
I have used if else,
I wanted to do was that to get another column containing these categories as primary,secondary,secondaryT and etc.But it seems not to work.
if ( SEX=="MALE" & SPORT=="CADET" & Bazett_formula <400) {"Primary"} else if (SEX=="MALE" & SPORT=="CADET" & Bazett_formula >400 ) {"Secondary"
} else if ( SEX=="FEMALE" & SPORT=="CADET" & Bazett_formula <400) {
"PrimaryT"
} else {
"secondaryT"
}
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It sounds like you may be using the tidyverse. If so, I suspect that the syntax you are looking for is handled better by case_when... This allows you to put a boolean, TRUE/FALSE/ statement and then ~ and the output. Just note that if your output includes NA, you will need to match the NA type to the other outputs, so NA_character for the cexample below
data %>%
mutate( new_categorical = case_when( variable < 40 ~ "Category 1",
variable >= 40 & variable < 50 ~ "Category 2",
variable >= 50 ~ "Category 3",
.TRUE ~ NA_character ) )
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There is software that could be used to do the sampling, but maybe someone has developed any Excel sheet to do the process easily. I would like to do a multi-stage including principally clusters and strata (age and sex).
Thanks in advance.
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Hai Javier,
Recently came across the site that really provided great insight into the statistics platform via excel addin. Hope this may be informative
Similarly, refer to the PAST open-source tool too.
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Hi all,
I have data of length and weight of fish.
IIsample created a weight-lenght relationship transforming the exponential formula (W=a*L^b) in linear applying the log10 (Log(W)=LOG(a)+b*LOG(L)) and created length and weight relationship based on sex (female and male).
The sample size between the sexes is different and i would like to compare the female and male models to see if there is any difference.
since the semple size is different I am not able to compare them using ANOVA function.
which test are you suggesting?
thanks in advance.
Danilo
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thanks a lot James for your comments and detail explanation.
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I am working on pollution studies in India using sea urchin as the major candidate..could you tell me how important it is to also consider the sex-based difference in bioaccumulation of pollutants in this organism..is it mandatory unless there is plenty of samples here.
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In general, one should study sex diifrences in marin species. In fish, for example, detoxifying enzames (eg CYT 459) is lower in females.
In sea urchin we have less experienc. But if you study also effects on reproductive success in relation to contaminants you should consider effect on sperm and eggs. Best wishe and success
Angela
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Could someone point me to a resource on sexing beetles, Bruchus sp. in particular? I know it should be fairly straightforward, however I have failed to find a good visual reference. We need to sex beetles for cage breeding experiments to ensure a consistent male to female ratio, so wee have do it without compromising their health and minimizing handling as much as possible.
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Hello,
In Bruchus, the sexes are readily separated. Only males have modified middle tibiae, which they are more curved than those of females and have distinct spines or plates on their edges which are not present in females.
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Hello, I am determining the sex of mice using the Sry gene on a 2% agarose gel. The figure shows that in lanes 12, 17 and 19 there are two faint bands. What do they mean? And there are faint bands that also suggest that they are female but there should be no mark. Could you help me with advice on how to improve sex determination? Regards
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Dear Dr. Ramirez,
1.)
You can try to adjust your annealing temp to be more specific by increasing it a degree or two.
Look at the Primer papers and its annealing temps and adjust your PCR protocal.
2.)
You may also from the looks of your PCR product so bright, drop your thermo cycle rounds of PCR a time or two and get more specific.
3.) Load a little bit less template DNA.
4.) Make sure there is no cross contamination by skipping wells of each samples, and using good tech.
Best Wishes and Good Luck,
Charles
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what are the indirect methods to calculate the total mortality rates using the age-sex structure dervied from the census?
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You must use the Coale-Trussell variant of the Brass' Method.
It can be found in several publications of CELADE, including documents produced by professors such as Domingo Primante, Víctor García and Dirk Jaspers.
You can also find an excelent description of the method for constructing life tables from information on adult data collected in censuses, presented by the mexican demographer Alejandro Mina.
Read the beautiful work of Sergio Camposortega Cruz: "Análisis demográfico de la mortalidad en México, 1940-1980"
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I have RNA-Seq data of piglets' small intestine, I want make sure about the sex of the animals. I therefore need to know if there is a tool or command line to used in Linux which can identify whether the sample is a female or male? I appreciate your time in advance
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Don't mention it.
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when the age and sex structure of the population is available, should it be systematically used to calibrate a sample ?
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I fully agree with you that the variables used for calibration must be correlated with the variables of interest. in fact, I put myself in the case of a household survey whose objectives are multiple.
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I am considering doing my masters thesis on this topic but as a new masters student, I would greatly appreciate some guidance. Thank you!
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Hi, I am working to a new project about the correlation between acute stress and inflammatory response. The design of the study will be an RCT, and I am planning to do a stratified random allocation to match participants for age and sex. The exclusion criteria are assessed in two phases: 1. phone screening, and 2. clinical interview with a psychodiagnostic battery. I want to screen a pool of 100 candidates, from which randomising a total of 40 participants. Do I need to administer the n.2 screening phase to all participants before randomising them? Otherwise, if I randomise the participants after the phone screening, and during phase 2 a participant is excluded, is there a way to replace him without invalidating randomisation procedure?
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My concern would be about self selection by volunteers or self exclusion. This was tried in 1930s by Gluecks using bathers on public beaches. Better to focus on a patient group needing treatment then allocate into A or B or Null. Then compare measurable outcomes.
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I have the following issue with a paper, which I think is very common:
We are studying the effect of a certain disease on white matter microstructure. We want to know if there is an effect of this disease in both (or either) males and females. We have run a whole group analysis, and after that split the group by sex to test the association in each sex separately. Our results are as follows: there is no effect in the whole group, there is an effect in males but not in females.
However, as pointed out by one of our reviewers, the correct way of testing sex effect is really to do an interaction analyses, and only split by group if the interaction term is significant. In our case there is no significant interaction term. Thus, if we choose this option, we have no effect in the whole group and no interaction effect. However, if we choose such an approach, we "miss out" on the effect of the disease on white matter in the males.
I guess this boils down to what the question really is. Do we really need to test if males and females differ significantly from each other (which is what an interaction term tests) to know if disease X has an effect on the brain in either sex? Would it not also be accurate to just split and report that we find an effect in males, but not females, and add that we cannot draw a conclusion about the females (as they might have been to low N) and not about males vs females either?
I am curious what your views are on interaction vs splitting groups.
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Hi!! Everytime I review a paper where the results can be split in "male & female" results, I ask the researchers to explain, for example, if other categories of analysis, such as age, ethinic background etc, would be applied as well to do some more splitting in order to perceive any kind of significant results that may be hide in a dicotomic gender split.
In my opinion, you have two options:
1) You can split and report that you find an effect in males, but not in females, and add that you cannot draw a conclusion about the females YET or
2) You can add to the analysis another categories if relevant (as age, social background, etc) and than compare each intersectional categories (example: 30 years male x 30 years female; 23 years caucasian male x 23 years caucasian female, etc) in order to explain that if it is really a gender difference that justify the different results between the groups, not others factors.
I suggest you to read "Better science with sex and gender: Facilitating the use of a sex and gender-based analysis in health research", by Joy L Johnson, Lorraine Greaves and Robin Repta.
Hope it helps. :)
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Hi All, I have a question regarding the choice of interaction vs subgroup analyses in the context of effect modification. If the effect modifier (e.g., sex) is highly correlated with or even deterministic of one or several covariates (e.g., parity, breastfeeding) in the regression model, would you obtain the stratum-specific estimates derived from the interaction model with the covariates controlled for, or would you report the estimates from subgroup analyses (in men and women respectively)?
If from the interaction model, how would you address the collinearity between the interaction term and the covariates? In the latter, do we need to worry about the sample selection issue (loose of sufficiency for estimating the standard error)?
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David Eugene Booth Thank you so much for your detailed explanations! I'll check the materials you recommended.
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It seems that knockout of many nonessential genes in zebrafish may lead to considerably reduced number of females -- sometimes it is difficult to get any female zebrafish in many litters. This situation could possibly involve the nutritional regulation of sex determination of zebrafish. Is there any study that has addressed this issue by, for example, assessing the likelihood ratio, clarifying the underlying mechanism, or providing a means to deal with this difficulty?
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Are you sure it's the KO's that are shifting the ratio towards males?
The ratio normally tends to shift towards females in optimal conditions: a lot of food, very clean water, etc. And more towards males in suboptimal conditions. As the sex is usually determined after fertilization, hatching, etc, you can influence the ratio.
I would suggest to make sure that your fish are very well fed and have really clean water and then check if your KO's keep producing mainly males.
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I’m embarking on a project to understand why students drop out of college. I think a survival analysis makes sense to use. But I’ve never done one. Before asking my institution for data I want to be sure I’m asking for it in the right form. So my questions are these: (1) can I use a mix of nominal (sex) and continuous (GPA) variables as predictors and (2) if I have these for each semester for ten years would I be able to predict the withdrawal (event) for each semester or should I do it for one specific time period at the end?
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Say what? The data are ALWAYS times and the associated censoring. Survival analysis is about time to an event. There are good references in the paper. I think you should start by looking up survival analysis. Perhaps that is not what you want. David Booth
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I 'm comparing between three groups of mice. Normally, I can do one way ANOVA with post-hoc test. However, I