With mascarpone, blueberry and lingonberry preserves, a huge fresh ginger root, cloves, nutmeg, almonds, whole wheat flour, flax seed, applesauce, milk and baking soda for the leavening.



Even though I added a good sized ginger root chunk, it's not spicy. Next time, I will probably add even more. I purposefully mixed the doughly roughly so that I would get the hard crusty exterior and the bread would be slightly crispy. I don't particularly like moist bread. They don't toast too well. Unfortunately, it wasn't dry enough because in spite of all these precautions it's rather moist. Maybe I should have waited for it to cool down instead of slicing through it like the hungry, impatient and greedy person that I totally am.

I finally found it. I don't know what took me so long. But this is amazingly effective.


Baking Soda
(1 pinch/sprinkle)

+

Coconut oil
(to dissolve/make a paste from the baking soda)


You can substitute the coconut oil with vaseline intensive care or perfume-free vaseline. It will work just as well. Apply liberally on odor-prone underarm areas. That's it. No more chemicals or plastic packaging waste or even dehydrating alcohol derivatives ever.

I just sprinkle some baking soda on my palm, mix with a spot of coconut oil and apply immediately after showering. It works so well that I now have trouble finding out what clothes I have worn and what clothes I haven't if I don't consciously toss them in the laundry bag. I would say it has eliminated around 99.8% odor.

Try it out, if you haven't already.

LastFM just sprang this on me and I was completely blown away!

Think he might be dancing in his grave?

Yo-Yo Ma & Andrew Bird


And music from the Appalachian Journey

Coding is time consuming. I didn't really consciously think about this till my advisor asked me about the roadblocks to my progress in my dissertation. I thought back and it occurred to me that I was

• learning a somewhat new language (R)
• trying to code in it using concepts from statistics that I already have
• trying to generalize code so that it could be less repetitive and automate chores and processing
• trying to splice other languages such as latex and perl to interact with R code
• learning THOSE other languages
• trying to prevent errors in their interaction
• trying to write functions that would serve a purpose similar to "macros" on SAS but yet be understandable to me at a somewhat-novice level.
• debugging to see where it all goes wrong when errors turn up
• differentiating what is my code's fault, what can be changed in R preferences, and what is inherently the fault of the R build on the particular linux distro I am using.

Why am I doing this when I have a data manager for the data I am dealing with? Because it's insane to go whining to the manager the minute you want some data processing done. He has own Ph.D. to complete and has limited time and innumerable demands. So there is really no option but to power through all of this on my own. Besides, I think population sciences are *really* about statistics and data processing even though population scientists who don't do all this dirty work may persuade themselves that it is just about interpretation. It is as much about quality control in the data and seemingly trivial logical checks as it is about analyzing super-clean data in the end and figuring out what it could mean in the bigger picture of million other studies. I think it's important to acquire this skill so I can use it in my future. Not everyone has a data manager, a software coding bloke and a statistician to always help them.

The process itself is exhausting because it's like learning a completely new language without having ANY experience in how the semantics work. Apparently some people have observed this exhaustion and set up sites like "Software Carpentry": It sets up a formalized framework within which you could learn these skills. I think courses like this should be mandatory to population sciences coursework. At some point, we need to realize that population sciences and bioinformatics are very similar in their scope and application. This is more true than ever as we progress rapidly towards HUGE datasets, complex data structures, multidimensional variables and the necessity of channeling all of this into interpretations simple enough to make sense to health-related and population policy-making.