HPCL and GAIL are customers of ISGEC Heavy #ISGEC

HELLO: If you pee and every time you finish, your urine looks like this picture, you may have a serious medical illness.

Foamy urine especially if it is persistent and noticeable over time may mean you have a serious kidney problem.

Let’s talk about this today...
RT for others.

What are the features of a normal urine?

A normal urine should have a light yellow colour, painless, odourless (or faint smell), steady stream and should ideally not foam when you pee.

When there’s something wrong, then you see changes in all the above normal features of urine.

For instance if urine becomes very deep yellow, it may mean you are dehydrated;

If urine becomes painful or smells really really bad, it may mean you have an infection;

If urine has a poor stream, especially in a man, it may mean a prostate problem like cancer.

Do you get me?

So today let’s talk about Foamy Urine.

What are the things that can cause urine to suddenly be foamy. As I said in the first tweet, it MAY mean a serious medical illness like a serious kidney problem.

First things first- it does NOT always mean a problem but it may as well.

Now what are the causes of Foamy Urine:

1. SPEED-
If you pee really fast or with a lot of force, it may make your urine more foamy than usual. The speed and force makes the urine generate a lot of bubbles which tend to almost quickly disappear as it is formed in the toilet bowl.

In Academia, people aren’t always supported when they need it. I want to share what @WesternU did when my husband died right after the pandemic shutdown. It should be shared with other uni’s as a model for empathy and proactive care when someone is in crisis.
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#AcademicChatter

This post is pretty bizarre, but it manages to hit on so many false beliefs that I've seen hurt junior data scientists that it deserves some explicit

(1) The notion that R is well-suited to "building web applications" seems totally out of left field. I don't feel like most R loyalists think this is a good idea, but it's worth calling out that no normal company will be glad you wrote your entire web app in R.

(2) It is true that Python had some issues historically with the 2-to-3 transition, but it's not such a big deal these days. On the flip side, I have found interesting R code that doesn't run in modern R interpreters because of changes in core operations (e.g. assignment syntax).

(3) "Most of the time we only need a latest, working interpreter with the latest packages to run the code" -- this is where things get real and reveal some things that hurt data scientists. If this sentence is true, it's likely because you don't share code with coworkers.

(3) Really is a broader issue in data science: people only think of what they need to do their work if no one else existed and code was never maintained. Junior data scientists almost always operate on projects they start from scratch and don't have to maintain for long.