Intermittent Fasting 16-8 for 8 Weeks in Resistance Trained Males – [2016 Study]

intermittent-fasting-and-8-weeks-of-resistance-training-2016-study

Researchers from universities in Italy, Brazil and the United States did a study comparing resistance trained (RT) athletes who engaged in intermittent fasting (16/8) with RT athletes who ate normally.

The experiment ran for 8 weeks and the study was published in the Journal of Translational Medicine in October, 2016. You can read it here.

My purpose with this post is to give some thoughts about this study. I also did a video review.

Physique Update and One of my Best Workouts this Year – [Nov. 2016]

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Nov. 26, 2016: I just had one of the best workouts of this year. I’ll detail the context as I find it interesting and I think it may be useful for future reference (my future reference).

William Banting’s Weight Loss Experiment [1864] – My Notes

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This short and free read, I’d recommend getting your hands on it immediately; you’re given a clear description from William Banting himself (1796 – 1878) about how he went from obese to normal weight in a matter of months. He was ~67 years old at the time of his n=1 personal experiment…

Here I’m going to share some of the notes I’ve taken from Banting’s booklet – that details his journey -, a writing he dedicates to the public at large, “entirely from an earnest desire to confer a benefit on my fellow creatures.” [1]

You can read it for free here.

Genetic Mutations and Diabetes – My Analysis of 115 Genomes

genetic-mutations-and-diabetes-my-analysis-of-115-genomes

Last week I began analyzing genotype and phenotype data available through OpenSNP, a platform where people share this type of information.

The first phenotype I looked into was about smoking.

Using Python I took the smoker status reported by users and correlated it with a mutation (rs1051730) in the nicotinic acetylcholine receptor alpha 3 subunit CHRNA3 gene. A few genome wide association studies (GWAS) linked this mutation to nicotine dependence, alcohol abuse, and susceptibility of developing lung cancer.

My point with the post was to offer a proof of concept and to reveal/interpret the data I got out of my Python analysis. I wanted to create a precedent so that others could freely use and improve my scripts and my approach.

Of course, if you’re a user of OpenSNP, you can gain a lot of insight by looking at your own genotype for this SNP (single nucleotide polymorphism) and correlate it with my findings. To see the exact details of what I did and to download the Python codes, go and read the post.

Anyhow, I decided to continue with another analysis.

Analysis of 243 Genomes – My First Report [Nov. 2016]

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About two weeks ago I learned about this website OpenSNP where people can share their genetic information and not only. It is similar to 1000genomes, but I think it is much more interesting to work with because aside of genetic information (SNP sequencing, exome, etc.) most users also share phenotype data; data is not anonymized. This is what sparked my interest.

With phenotype data and user’s genetic mutations – SNPs – (or other relevant genetic information), I could run analyses and find possible correlations. This is applied big data.

In this post, I’ll explain how I conducted my first analysis. I want to provide an outline with enough relevant details so I can have a reference point to make things easier in future analyses. Of course, I could simply do this in private but I’d rather post it on the blog so that others who are interested to run similar analyses can have starting point.

This involves: knowledge of genomics, genomics related software and raw data formats, programming, and a lot of patience.

30 Health and Fitness Books – Free Bundle

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You have to subscribe to Buckbooks’ email list (not mine)! The promo is on for 3 more days (ends circa Nov. 10, 2016).

If you’re on my email list, you already know about this promo as I broadcasted a message immediately it became available. If you’re not on my list and didn’t know about this, listen up.

I joined forces with a dozen plus authors and we give our books for free – 30 in number. This bundle is available via Buckbooks who made this possible. To get the bundle you have to sign-up (for free) to their list. Once that’s done, wait for the email. Here are a few titles from the bundle:

My Encounter with Curiosity, the Mars Rover

As I came out of the number 1 subway station in Lower Manhattan on Wednesday evening I encountered a scene that was different to what I was used to.

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Instinct had me enter, but I couldn’t stay long because I was already late for an evening lecture at the genome center right across the street. So I rushed asking for details and deciding I was going to come back another day.

After-thoughts of a 44-hour fast – Just completed [Oct. 2016]

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I took a trip to DC earlier this week and I decided to take a break from eating for the whole duration – until returning to NYC.

Radiotolerance Lessons from the Tardigrades

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Image: female tardigrade containing eggs.

Hashimoto and colleagues (2016) published an article in Nature recently:

Extremotolerant tardigrade genome and improved radiotolerance of human cultured cells by tardigrade-unique protein

Tardigrades, a.k.a. water bears, are some of the most extreme organisms, capable of surviving in the most un-habitable environments and being exposed to insults that would kill other living beings. Examples include: very high and very low temperatures, high doses of radiation, high pressure, outer space, and others.

Here are some of the particularities (in terms of gene expression) of tardigrades:

The Hallmarks of Cancers #1 – Deregulating Cellular Energetics

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I wrote a moderate-length review of Hanahan and Weinberg’s papers a few months ago.

In their papers, they discuss the most common similarities among cancers and they base their writing on ~5 decades of research in this field.

While each cancer is unique, especially if we view it from a genetics standpoint, Hanahan and Weinberg discuss 8 hallmarks they found to be common in cancers.

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