Molecular biology is the new radioactivity

Culture plays an important role in shaping our society. Throughout history, stories, poems and songs have commented on the world around them but have also inspired change through self-reflection. This seems partly to be satisfying a basic human need for a “structured” way to interpret our world. More recently, the presence of communications media that are able to virtually reach everyone in the world have allowed some forms of culture to dominate. The prime example is the so called pop culture: mostly movies, but also comic books, books and more recently even memes.

If one assumes that pop-culture as a whole can be a reflection on how its creators perceive our world and its future, looking at overarching trends might prove interesting. For instance, how’s pop culture perceiving science and its influence on society? An easy starting point is a hallmark of last century, which still has a strong influence in pop culture: World War II. Every year there are a number of movies with it as a setting, either to revive some sort of nationalistic pride or for plain old “entertainment”. I believe there’s something more to it, related to the huge influence the war has had on essentially all branches of sciences, from space rockets up to cryptography. One development in particular has for decades haunted pop culture: nuclear physics. Even a superficial survey of a few mangas should make clear how nuclear explosions and fallouts have been sublimated in many forms. But it doesn’t stop in Japan; American comic books from the 50s up to the 90s have featured super-heroes whose origins are overwhelmingly due to the effects of radioactivity. It’s easy to conclude that the fear and anticipation of its impact on society was translated in pop culture. Whether this influence stopped after the Chernobyl and Three Mile Island incidents is perhaps an open question.

Can you tell what this looks like? (from Akira)

What is pop-culture now perceiving as the current scientific bleeding edge? I’m tempted to say that molecular biology is the new radioactivity. Reflecting on the gradual takeover of molecular biology over biological sciences as a whole in the decades after the discovery of the structure of DNA, pop culture has started to display increasing anticipation and fear about its impact on society. The hallmark of this shift is most likely the ‘94 movie Jurassic Park, showcasing the power and ultimate hubris of “tinkering with mother nature”. The very same “radioactive” American superheroes have gradually been rebooted (multiple times over) to have their origin rewritten to be due to some failed experiment with some human/animal/alien DNA.

Bingo! Dino DNA

if dinosaurs and spider-men are perhaps very crude examples, two recent works of pop culture have captured my attention for being slightly more sophisticated. The first example is the movie Annihilation, which by itself is a great exercise in distilling and focusing an existing book. In it Alex Garland (writer/director) poses a simple question: how would a cancer look like if it came in the shape of an extra-terrestrial entity? How would plants and animals change when they are absorbed by a tumor the size of natural park? Despite a few sentences here and there that will certainly make a hardcore molecular biologist cringe, the movie brilliantly succeeds in portraying the irrational and senseless threat of cancer.

The second example is unlikely to have made the international stage, and its connection to molecular biology is rather comical for those following genomics discussions on twitter: the Italian TV-series “The Miracle” (“Il Miracolo”). Incidentally the series also comments on the uncertainties of the EU experiment: at the verge of an “Italexit” referendum, the prime minister is informed that a statue of the Virgin Marie weeping liters of blood every hour has been found1. Apart from the effect of revealing the presence of this unexplained phenomenon to the world and to the referendum itself would have, the first question that comes to mind is: “whose blood is it?”. Instead of crossing the sequence data with government and public repositories (as law enforcement are increasingly doing), the authors came up with a dodgy online service that could predict… facial features from DNA. I have no clue as to whether the authors were aware of the “facial features prediction” paper, but its critics would be happy to know that it didn’t work so well in this TV series. I’ll leave to the reader to figure out which other controversial molecular biology technique ended up working here.

[1] it sounds very silly, but Italy has an established history of weeping virgin Marys, and prime ministers gambling their careers on a referendum

Methods section driven reproducibility

A cornerstone of the scientific method has always been the ability to draw the same conclusions after the execution of different experiments. I would very much like to say that there is a consensus in the scientific community on how to call such a process but unfortunately that doesn’t seem to be the case. The terms “reproducibility”, “replicability” and “robustness” are often used interchangeably and different people might rank them differently depending on how they interpret them. Luckily, a recent paper cleverly proposed to stick to “reproducibility” to describe the process as a whole and to name its different flavors by adding a prefix. In short, Goodman et al. indicate the following kinds of reproducibility in science (the short summaries are mine):

  • Methods reproducibility: giving sufficient details about the experimental procedure and the processing of the data so that the “exact same” results can be obtained
  • Results reproducibility: carrying out an independent study with “very similar” procedure and obtaining “close enough” results as the original study
  • Inferential reproducibility: drawing “qualitatively” similar results from independent studies or a reanalysis of the same data

In the specific area of computational biology, the requirements to meet these three objectives can be more precisely defined:

  • Methods reproducibility: providing “machine code” that give exactly the same output given the same input
  • Results reproducibility: providing all the relevant details about the algorithms used so that they can be re-run/reimplemented and give quantitatively similar results on the same or different data
  • Inferential reproducibility: providing an interpretation of the results of an experiment so that it can be qualitatively compared with another study

It’s easy to see how the latter flavor of reproducibility is the most valuable, as getting to the same conclusions using different data or even completely different experimental strategies can sometime provide further support by itself. Needless to say that is also the one that requires the most work and resources to achieve.

Regarding methods reproducibility, it has become pretty fashionable in computational biology; many journals are explicitly requesting authors to deposit all computer code as supplementary material. The extreme case being providing either VMs or so called containers to ensure that the specific computing environment does not alter the final result, leading to perfect methods reproducibility. This is an important thing to aspire for, especially to avoid scientific fraud (or bona-fide errors), and many people have proposed technologies to make this relatively easy to achieve. Despite all this, I believe that in many cases the emphasis should be on achieving better results reproducibility over perfect methods reproducibility. This usually comes in the form of none less than the good old methods section of a paper1. If the algorithms used in an experiment are explained with sufficient detail, it will only be (relatively) trivial to reimplement them to produce very similar results on different data, thus reproducing (in the “results” sense) the original paper. What’s more interesting, writing an implementation of an algorithm from scratch is a great exercise and provides a great way to properly understand how a method works, not to mention the possibility to improve it. In fact, I recently had to reimplement some algorithms that were very well described in other paper’s methods sections (part of this, and the whole of this with some help)2. In the process I have better understood the algorithms and I ended up making improvements and extensions. It also has convinced me that trying to reimplement an algorithm from a paper could be an interesting part of a computational biology class. All of this is simply not possible through methods reproducibility, unless a thorough inspection of the source code is made, which in many cases can be a true nightmare. Even the most advanced container technology or programming language will eventually fade, but a well-written couple of paragraphs will continue on for a long time.

[1] or the documentation of your software package, or a chapter of a book or an interactive blog post

[2] our former colleague Omar was particularly good in reimplementing existing methods to make them more user friendly and extensible, like SIFT or motif-x

The periodic table

A few months ago the IUPAC (International Union of Pure and Applied Chemistry) had to take a decision on the only apparently very simple task of giving a name to the latest discovered chemical elements. Even a basic understanding of chemistry and the way the periodic table is made will make apparent how such decision is both of little significance and at the same time irrepetible. Of little significance, for those elements are only artificially made and have a short and turbulent life before becoming a lighter and more stable element; almost irrepetible, because the number of new element “discovered” is probably reaching the limit of what is humanly possible.
Some people argued that one of those elements should have been called Levium, in honour to Primo Levi’s and the short stories of “The periodic table” (“Il sistema periodico” in Italian), unfortunately with little success.

Primo Levi, who is mostly known for his recollection of his deportation to the Auschwitz concentration camp, possesses two only apparent antithetical qualities as a writer: he is both a distant observer and a relentless moral agent. The former quality appears somewhat wrong when used to describe the hopeless struggle that characterized the inmates of the annihilation camps, but such distance acquires sense when used to derive fundamental truths about human nature. This is most evident in the “I sommersi e i salvati” (“The drowned and the saved”) chapter of “Se questo e’ un uomo” (“If this is a man”).
A similar approach is taken in the main theme of “The periodic table”: the somewhat technical recollection of the experiences of the author as a chemist (mostly in the varnish industry) are used to again convey some very sharp truths about human nature, but most of all what science is.
In many occasions throughout the book (each chapter revolving around a particular element), Levi demonstrate to know all too well the high of intellectual discovery, and chooses to stay far away from collective discoveries and scientific enterprises, but to focus on the solitary work of single chemists, which is very similar in spirit to the work of the first alchemists. This is especially true, Levi claims, when considering the daily struggle of a chemist working in an industry, whose day-to-day battle against matter itself eventually only leads to very occasional small victories and surely to burn-out with age; “Chromium”, “Nickel” and “Silver” are very good examples of chapters where this concept is exposed. Other chapters are instead more intimate and moving, were chemistry is put aside to give space to Levi’s moral compass, forged by a life of terrible experiences and constant observation of human nature. Some of those chapters, like “Argon” and “Tin”, are exhilarating accounts of the family and friends of the author. Others instead recall more dramatic moments in the author’s life, and are probably the best parts of the book. In “Iron”, a fellow chemistry student with a passion for climbing is used to show what is like (and what’s the cost) of being free; the opposite concept is presented in “Gold”, when the author has been captured by fascists and fears that he would soon die. In “Cerium” an episode from the Nazi lager is recalled to remind that some basic (and maybe irrational) form of human will can persist even in the face of hopelessness. Finally in “Vanadium”, the incredible and fortuitous exchange between Levi and one of the German civilians with whom he interacted in Auschwitz shows how morality is a complex matter even when reason is indisputably on one side of the argument.

I could not recommend this book enough, especially to anyone accustomed to rational thought. Even though it would be impossible to replicate this formula, I cannot but wonder how a similar book would look like for disciplines like physics (a chapter for each particle?) or even “messier” sciences like biology (a chapter for each species?). It could actually be an interesting exercise for a crowd-sourced book, where stories on a single “unit” of each discipline are contributed by different people. I would surely like to write a chapter about one or two bacterial species.

Phenotype Microarray data analysis in BioPython

Everyone who does computational biology and has wrote at least one Python script probably knows about the BioPython library. I personally remember going “Oh!” some years ago when I gave up writing my own (horrible, terrible, clunky) GenBank file parser and discovered it. Since then it has been a central part of almost all small scripts I needed to write. Recent versions have become even more useful, with the inclusion of a very cool KEGG API wrapper, which has the side-effect of putting together two well-designed bioinformatics software together!

It is then with great pleasure that I’m announcing the addition of the Bio.phenotype module to BioPython, starting from version 1.67. The module allows to parse and write the outputs of Phenotype Microarray experiments, as well as to run some simple analysis on the raw data. Even though I have published another software in the past to run the same analysis (plus some more), I thought that a simpler library would prove useful for many, and that having it as part of BioPython would make it more easily accessible. Moreover, from a software development perspective it is worth noting that BioPython is following very strict practices, which ensure that code is properly written, tested and maintained. This is all possible thanks to the great work of the BioPython community in general and of Peter Cock in particular.

Example PM well

An example well from a Phenotype Microarray experiment, showing the parameters estimated by the Bio.phenotype module (code is in the tutorial at the end of the post).

So, I really hope that this small library will prove useful to anyone with Phenotype Microarray CSV files collecting dust in their filesystem. To make it even easier I’ve posted a small tutorial here, which also includes some downstream analysis and plots that are not covered in the BioPython manual (the tutorial is also embedded at the end of this post).

Happy analysis!

Design (in computational biology) matters

Unlike the usual attitude among bioinformaticians (weep at the horrible state of the software in the field, the proliferation of standards, you name it), I want to write a post to celebrate the amazing leaps forward in the field of bacterial comparative genomics, and what can be learned from it.

A basic recap
To everyone not familiar with bacterial genetics, it’s useful to explain a bit the very peculiar genetics of microorganisms. To date, the best metaphor that I’m aware of has been  coined by Prof. Peter Young, by comparing bacterial genomes to smartphones. A species would be represented by a model (i.e. an iPhone), which has then a very similar OS between strains (representing the conserved genes, or core), but it differs in the number and types of apps installed (representing the so-called dispensable or accessory genes). The ensemble of OS and all the different apps installed in each “phone” is termed “pangenome“. It’s of course a little bit more complicated than that, but this metaphor is useful to explain what microbiologist think of the different gene content inside the same species: different “apps” could potentially mean adaptations to specific niches. Several theoretical and experimental works have proven that this postulate is tendentially true. Pinning down which genes might confer a competitive advantage in a certain environment is a difficult task though; sometimes up to thousands of different “apps” are “installed”
between different strains of the same species, usually with little or no annotation (think of an app in a foreign language with no clear user interface!).

These are actually different species, but you get the idea!

These are actually different species if we follow the metaphor, but you get the idea!

Another, maybe easier, use of the pangenome concept concerns phylogenesis; genetic relationship between strains can be constructed by aligning conserved genes (slightly different OS subversions let’s say) or by comparing which accessory genes are shared (phones with overlapping “apps” could potentially operate in a similar way).

These two approaches represent the vast majority of the analysis that are being carried on bacterial pangenomes; so much that they are now becoming the routine. As any other bioinformatics task, anything that is done more than once will inevitably become a “pipeline”, with different levels of flexibility and quality. How good is the established “pangenome pipeline”, if there’s any?

The good ‘ol days
Back in 2011 I was a PhD student in Florence and had to make sense of a large (for the time) set of Sinorhizobium meliloti strains. The things we wanted to study about this  (interesting!) symbiotic species required to annotate the newly sequenced genomes, define the pangenome (what genes are part of the OS, and which genes are apps) and finally get a phylogenetic tree of the core and accessory genome.

At the time most of the steps required to achieve these relatively simple goals were run “manually”: the genomes had to be assembled/annotated by knitting together a plethora of different tools, such as prodigal to get gene models, rnammer to predict rRNA clusters, blast2go, interproscan and KAAS for functional annotations. Sure, at the time automatic web-based pipelines were available (such as Rast), but with limited availability, flexibility and long waiting times; and if you imagine having hundreds of strains instead of tens, you immediately see how this approach cannot scale up.

A blast2go run on a single genome took a lot of time to complete

A blast2go run on a single genome took a lot of time to complete.

Then to get the pangenome, the appropriate orthology algorithm has to be chosen; accuracy is not really a problem, as strains are usually so close to each other that conserved genes can be very easily detected (so easy that you can write your own implementation). The problem is mostly computational: the algorithm has to be fast and not resources intensive, especially when the number of genomes scales up. As a way of example, using InParanoid/Multiparanoid becomes quickly problematic for the exponential growth in the pairwise InParanoid jobs and the amount of memory required by Multiparanoid. Similar limitations apply to other routinely used orthology algorithm (say OMA), designed and used on inter-species analysis.

The last step in the pipeline required running an alignment of each conserved gene, concatenate them and then derive a phylogenetic tree. This step was somewhat less stressful, but still required some scripting and could become complicated when scaling up.

To summarize, a few years ago almost all the necessary tools were present, but requiring a substantial amount of time to get to the desired results, not to mention the risk of propagating any trivial mistake in any of the analysis step down to the final conclusions of the analysis. You might already imagine what is needed to overcome these problems…

The glorious present and future

Fast forward to 2015, where to get the same results that we got 4 years ago I can use the following tools:

  • The Spades assembler to not worry too much on choosing the optimal value of k for the de Bruijn graph
  • Prokka to transform a list of contigs in useful (and tidy-ready-to-submit-to-genbank) annotations in standard formats
  • Roary to quickly and scalably get the pangenome (and its alignment!) sorted out

It is worth noting that now the whole pipeline can be basically run with three command line calls, and that it shouldn’t take longer than a couple of hours on my 2011 dataset (depending on how many cores you can lay your hands on). This leaves plenty of time to focus on the actual biology of these bugs and to design and carry on more complex and interesting computational analysis.

The Quokka, the marsupial from which Prokka takes its name from

The Quokka, the marsupial from which Prokka takes its name from

But how exactly each one of this three tools has succeeded in retrospectively making my life so easier? The secret is not that they provided any particular strong breakthrough, but instead they all three have focused on design, usability and scalability. In particular:

  • Spades is amazingly robust, very well documented and maintained, runs relatively faster and with few errors, thus making it trustworthy
  • Prokka encapsulates pretty much every tool you might need to have a decent annotation and it’s again amazingly configurable and well documented/maintained
  • Roary takes a series of clever shortcuts on clustering gene families, taking advantage of the high similarity between strains, thus dramatically reducing computation time and requirements. Plus it works best when fed with Prokka’s outputs, thus encouraging people in the field to adapt to this new standard pipeline

It’s also worth noting that all three of this tools make extensive reuse of existing tools: Spades uses BWA for the error correction step, Prokka is basically (a very good) wrapper around all the tools needed to annotate a genome, and Roary as well. Not so much about re-inventing the wheel then, but more on making it better. I submit that as biology shifts towards larger and larger datasets only the implementations that heavily focus on good design, scalability and robustness will prevail.
We’re all advised…

Appendices to “Guns, Germs and Steel”

A particularly exciting part of scientific writing involves broad views on earth and human history; basically coming to very insightful conclusions starting from sparse and specific experiments. This is especially true when using archaeological data, which is very sparse and incomplete by its very nature. A prominent example of such broad insight on human history is “Guns, Germs and Steel” by prof. Jared Diamond, which represents a successfully attempt on removing racial prejudices regarding the reasons why western countries “succeeded” in conquering other civilizations. The most notable example is of course the south-american empires, but many other examples within African and Oceanian civilizations are presented. The bottom line of the book is relatively simple: the presence of domesticable plants and animals, together with a geographical configuration enabling exchanges boost technological advances. The relative western immunity to some infective agents derived from cattle domestication is also indicated as a decisive factor in the western expansion in America.

The book also features some pretty interesting bits, such as the accurate description of how a handful of Spanish soldiers kidnapped the Inca emperor Atahualpa (surrounded by 7000 soldiers!), and more interestingly how the Chinese empire (whose technological level has always been on par, if not more advanced than western civilizations) decided to abruptly stop naval explorations, therefore possibly changing the world’s history with such single minor decision.

Many critics to this book focus on the lack of clear and specific experiments that could confirm some of the most challenging conclusions of the author: Tom Tomlinson summarizes such critics by saying “it is inevitable that Professor Diamond uses very broad brush-strokes to fill in his argument”. It doesn’t necessarily need to be that way though: in another book by Walter Alvarez, every single experiment that led to the conclusion that the cretaceous mass-extinction was caused by a giant rock falling from the sky is clearly outlined and explained, including other quite convincing alternate theories. (As a side note, the book has the best name ever invented: T-rex and the crater of doom).

Given the use of this “broad brush-strokes”, it is inevitable that new experiments will eventually pop-out and provide details that could change the interpretation given by professor Diamond, maybe not completely, but at least by posing significant challenges, or the need to update parts of the book.

By chance I stumbled upon some very interesting studies that would very well fit as appendices to the book. The first study involves tuberculosis, which is one of the illnesses that badly affected south-american populations after contact with the conquistadores. The first deviation from Diamond’s theory is the fact that Mycobacterium tuberculosis (the bacteria causing the disease) was probably transmitted from humans to domesticated animals and not the contrary; the other more challenging discovery is the presence in Peru of human bones showing clear signs of tuberculosis some 300 years before Colombo set foot in the Americas. Luckily enough the bacterial DNA in those bones was still readable and allowed the authors of this study to conclude that tuberculosis was brought in Peru by… sea lions. This study itself is also another example of a huge insight coming from sparse and incomplete data: much of the conclusion relies on 5 non-synonymous SNPs shared between the Peruvian strain and those that infects modern-day sea lions; there are of course a number of other experiments in the article, making the conclusion pretty robust. It is entirely possible that the distance with the “regular” western tuberculosis was such that native populations had less resistance, but it also shows how new emerging experiments can threaten parts of a convincing theory. The devil is indeed in the details.

The other three studies all came in this week, as I attended a very interesting talk by Eske Willerslev on the analysis of ancient genomes (the oldest so far being a 700’000 years old horse!). The first study somewhat challenges Diamond’s idea that most of the large mammals have gone extinct by human intervention (thus reducing the availability of domesticating cattle for some civilizations), but could instead have been caused by a drastic reduction in the presence of some protein-rich plants after the end of the latest ice age. The other quite amazing couple of studies suggest that there have been contacts (and you know, admixture) between south-americans and Polynesians: this conclusion is based on two specular experiments: genotyping of natives from Rapanui and of two individuals belonging to the Brazilian Botocudos population (which actually seem to show no signs of native american origin). According to the genetic data this encounter has happened no later than about 1400. This exciting conclusion promises to revise the way human migrations in the new continent are currently taught, and it is also very likely that new surprises will pop out sooner or later.

P.s. I’m pretty sure that there will be many other studies out there challenging Diamond’s book details that I completely ignore 🙂

Congo red

This article is going to mix three things that apparently have little to do with each other: art, war, and microbiology.

As in the previous post, it all started with an art exhibition at the Strozzina museum in Florence: as part of the exhibition called “Unstable territories“, a room was all covered in black and featured several screens in the middle. A wild and alien landscape was being screened: it featured hills filled with trees and grass fields, all red. Some of the screens started showing soldiers marching through refugee camps, while tanks and guns were firing in the distance; all the people were looking at the camera in complete silence. A few dead bodies on the side of the roads were shown too, surrounded by fluorescent red grass. The piece is called “The enclave” by Richard Mosse, shoot in Congo using an infrared film used by the army to spot disguised weapons. The objective of the piece is quite straightforward: by showing an environment full of what resembles blood, the impact of the war on the population is exposed for everyone to see. The beautiful and quiet landscapes are transformed into a nightmare.

Image-111

A few months later, an article suggested that, instead of increasing the awareness about the too-often forgotten war in Congo, the art piece caught the interest of many people only because of the peculiar effect obtained through the use of infrared film. I first thought that it was wrong, but I soon realized that I knew close to nothing about that subject; kindly enough, the author pointed out a truly complete book on that war, called “Dancing in the glory of monsters“. The author does a great job in telling the long and intricate story of this war, going from the responsibility of the international community and state corruption down to the dreadful stories of the single civilians.

After reading that book, a few considerations come to mind: first of all, it is true that the work of Richard Mosse cannot be fully appreciated without a minimal knowledge on this horrible conflict. Among other things, the fact that the victims of the many mass killings have been buried in a hurry results in countless nameless graves that have been eaten by the jungle; the symbolism of the red landscape acquires then additional depth, pointing out the long trail of death that the war has brought. But something else quite unusual comes to mind after reading this story, which relates to conflicts in other parts of the world (a prominent example is the conflict in middle-east). We tend to pick a side in many of the traditional conflicts, for cultural reason or even just family tradition; not on this one (at least for me). Being such an unknown story, and given the horrors that have been perpetrated by all sides, the only possible side to pick is the civilian population. In fact, picking sides in the “traditional” conflicts after reading this book feels just wrong.

Now’s the turn of microbiology: in a visit to the Typas lab at EMBL, I’ve joined some experiments measuring biofilm formation on some bacterial strains. In this essay, a red dye is added to the agar plate, which binds to biofilm components: if a bacterial colony makes it, it turns red. Surprisingly, the name of the dye is Congo red, named by the German company Bayer in the heat of the colonization in Africa (perhaps it’s time to use another name in publications?). Looking at the agar plate doesn’t help but think about the work of Richard Mosse, and how strange this connections are.

Getting up-to-date in science

Keeping the pace with the current flow of scientific publications is a herculean task; the number of journals to keep track of is growing every year. Moreover, in some fields the number of people doing research might as well be higher than some small nations. Despite this difficulties, finding (and reading, you lazy!) the latest relevant literature is very important for scientific success. For instance, it helps in getting the latest research vibes and avoids proposing the same ideas already pursued by someone else. It also helps a lot in writing the introduction of your next paper…

Every one has his own way of keeping up to date; here’s my personal list of things I do. Any suggestion or addition is very welcome.

  • Pubmed searches: this method is very specific and effective. Just make a pubmed search on one of your research topic while logged in; save it and set up a weekly email with all the new articles that have shown up in the last week. Tip: make the search specific enough to have maximum ~20 results per week; better to have many specific emails than just a really fat one.
  • Pubchase: a very recent service that reads your library (either mendeley or a good ol’ bibtex file) and sends you a weekly email with recent publications that match your profile. Less predictable, but works quite well.
  • A (very) few rss feeds: this option is less frequently used, because is less specific and can grow pretty quickly, depending on the number of journals. On the other side may lead to completely unrelated but inspiring works.
  • A Twitter lists: I keep all the scientists I find interesting in a quite big (and private, sorry) list, which I keep on a tweetdeck column. Some interesting papers came out just through this source, and sometimes even before they got published, as pre-prints. The signal-to-noise ratio is absolutely low, but a casual scrolling with the morning coffee never hurt anyone.

There are other options that I’m not currently using, like the Google scholar updates: I have just my articles in my scholar library, so I get really boring Genome Announcements papers. I’m pretty sure that Google is more than able to set up a recommendation service if given the right library.

That’s it! Now go read something interesting!